Overview

Dataset statistics

Number of variables18
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory163.9 B

Variable types

Text1
Categorical2
Numeric15

Dataset

Description공무원 직종별(일반직, 정무직, 별정직, 경철, 소방, 교육직, 기능직, 공안직, 군무원, 연구직 등), 지역별(서울,부산,대구,광주,인천,대전,세종,울산,제주 등) 퇴직자 수 데이터입니다.
URLhttps://www.data.go.kr/data/15053015/fileData.do

Alerts

is highly overall correlated with 별정직 and 11 other fieldsHigh correlation
정무직 is highly overall correlated with 별정직 and 2 other fieldsHigh correlation
별정직 is highly overall correlated with and 11 other fieldsHigh correlation
일반직 is highly overall correlated with and 12 other fieldsHigh correlation
경찰 is highly overall correlated with and 10 other fieldsHigh correlation
소방 is highly overall correlated with and 10 other fieldsHigh correlation
교육직 is highly overall correlated with and 2 other fieldsHigh correlation
법관검사 is highly overall correlated with and 8 other fieldsHigh correlation
기능직 is highly overall correlated with 군무원High correlation
공안직 is highly overall correlated with and 8 other fieldsHigh correlation
연구직 is highly overall correlated with and 6 other fieldsHigh correlation
지도직 is highly overall correlated with 공중보건의High correlation
계약직 is highly overall correlated with and 10 other fieldsHigh correlation
공중보건의 is highly overall correlated with and 6 other fieldsHigh correlation
기타 is highly overall correlated with and 6 other fieldsHigh correlation
남여구분 is highly overall correlated with 일반직 and 2 other fieldsHigh correlation
군무원 is highly overall correlated with and 11 other fieldsHigh correlation
군무원 is highly imbalanced (75.9%)Imbalance
has unique valuesUnique
일반직 has unique valuesUnique
정무직 has 22 (64.7%) zerosZeros
별정직 has 2 (5.9%) zerosZeros
경찰 has 2 (5.9%) zerosZeros
소방 has 3 (8.8%) zerosZeros
법관검사 has 12 (35.3%) zerosZeros
기능직 has 20 (58.8%) zerosZeros
공안직 has 5 (14.7%) zerosZeros
지도직 has 6 (17.6%) zerosZeros
공중보건의 has 17 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-12 13:54:27.278396
Analysis finished2023-12-12 13:54:52.028059
Duration24.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T22:54:52.160440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters68
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row부산
4th row부산
5th row대구
ValueCountFrequency (%)
서울 2
 
5.9%
강원 2
 
5.9%
전남 2
 
5.9%
전북 2
 
5.9%
경남 2
 
5.9%
경북 2
 
5.9%
충남 2
 
5.9%
충북 2
 
5.9%
경기 2
 
5.9%
부산 2
 
5.9%
Other values (7) 14
41.2%
2023-12-12T22:54:52.524780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

남여구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
17 
17 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
17
50.0%
17
50.0%

Length

2023-12-12T22:54:52.674250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:54:52.822731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
17
50.0%
17
50.0%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1617.4412
Minimum260
Maximum8496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:52.943636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum260
5-th percentile336.85
Q1737.25
median1064
Q31775.75
95-th percentile5299.55
Maximum8496
Range8236
Interquartile range (IQR)1038.5

Descriptive statistics

Standard deviation1706.7174
Coefficient of variation (CV)1.0551959
Kurtosis8.0357693
Mean1617.4412
Median Absolute Deviation (MAD)499
Skewness2.6950121
Sum54993
Variance2912884.1
MonotonicityNot monotonic
2023-12-12T22:54:53.094571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8496 1
 
2.9%
1867 1
 
2.9%
1169 1
 
2.9%
601 1
 
2.9%
1566 1
 
2.9%
815 1
 
2.9%
1951 1
 
2.9%
896 1
 
2.9%
1088 1
 
2.9%
1697 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
260 1
2.9%
331 1
2.9%
340 1
2.9%
409 1
2.9%
414 1
2.9%
580 1
2.9%
601 1
2.9%
685 1
2.9%
732 1
2.9%
753 1
2.9%
ValueCountFrequency (%)
8496 1
2.9%
5321 1
2.9%
5288 1
2.9%
3627 1
2.9%
2063 1
2.9%
1951 1
2.9%
1867 1
2.9%
1841 1
2.9%
1802 1
2.9%
1697 1
2.9%

정무직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7352941
Minimum0
Maximum55
Zeros22
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:53.207414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile19.95
Maximum55
Range55
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.830389
Coefficient of variation (CV)3.1671908
Kurtosis14.410656
Mean3.7352941
Median Absolute Deviation (MAD)0
Skewness3.8654698
Sum127
Variance139.95811
MonotonicityNot monotonic
2023-12-12T22:54:53.306627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 22
64.7%
1 6
 
17.6%
55 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
44 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
ValueCountFrequency (%)
0 22
64.7%
1 6
 
17.6%
4 1
 
2.9%
5 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
44 1
 
2.9%
55 1
 
2.9%
ValueCountFrequency (%)
55 1
 
2.9%
44 1
 
2.9%
7 1
 
2.9%
6 1
 
2.9%
5 1
 
2.9%
4 1
 
2.9%
1 6
 
17.6%
0 22
64.7%

별정직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.764706
Minimum0
Maximum295
Zeros2
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:53.450914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q13
median12.5
Q324.75
95-th percentile111.9
Maximum295
Range295
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation56.06601
Coefficient of variation (CV)2.0193266
Kurtosis16.66044
Mean27.764706
Median Absolute Deviation (MAD)9.5
Skewness3.9005976
Sum944
Variance3143.3975
MonotonicityNot monotonic
2023-12-12T22:54:53.593459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 5
14.7%
2 4
 
11.8%
20 2
 
5.9%
25 2
 
5.9%
0 2
 
5.9%
5 2
 
5.9%
14 2
 
5.9%
295 1
 
2.9%
86 1
 
2.9%
22 1
 
2.9%
Other values (12) 12
35.3%
ValueCountFrequency (%)
0 2
 
5.9%
2 4
11.8%
3 5
14.7%
4 1
 
2.9%
5 2
 
5.9%
7 1
 
2.9%
8 1
 
2.9%
12 1
 
2.9%
13 1
 
2.9%
14 2
 
5.9%
ValueCountFrequency (%)
295 1
2.9%
160 1
2.9%
86 1
2.9%
49 1
2.9%
41 1
2.9%
28 1
2.9%
26 1
2.9%
25 2
5.9%
24 1
2.9%
22 1
2.9%

일반직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583.17647
Minimum87
Maximum2477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:53.738509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile130
Q1260
median404.5
Q3733.25
95-th percentile1502.2
Maximum2477
Range2390
Interquartile range (IQR)473.25

Descriptive statistics

Standard deviation524.24754
Coefficient of variation (CV)0.8989518
Kurtosis5.8406022
Mean583.17647
Median Absolute Deviation (MAD)250.5
Skewness2.2370311
Sum19828
Variance274835.48
MonotonicityNot monotonic
2023-12-12T22:54:53.892410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2477 1
 
2.9%
728 1
 
2.9%
500 1
 
2.9%
226 1
 
2.9%
667 1
 
2.9%
283 1
 
2.9%
984 1
 
2.9%
370 1
 
2.9%
397 1
 
2.9%
735 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
87 1
2.9%
91 1
2.9%
151 1
2.9%
152 1
2.9%
156 1
2.9%
216 1
2.9%
226 1
2.9%
240 1
2.9%
253 1
2.9%
281 1
2.9%
ValueCountFrequency (%)
2477 1
2.9%
2121 1
2.9%
1169 1
2.9%
1018 1
2.9%
984 1
2.9%
881 1
2.9%
826 1
2.9%
780 1
2.9%
735 1
2.9%
728 1
2.9%

경찰
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.76471
Minimum0
Maximum685
Zeros2
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:54.025985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q13.25
median29.5
Q3181
95-th percentile411.7
Maximum685
Range685
Interquartile range (IQR)177.75

Descriptive statistics

Standard deviation161.52494
Coefficient of variation (CV)1.4715563
Kurtosis5.4642645
Mean109.76471
Median Absolute Deviation (MAD)29
Skewness2.2230539
Sum3732
Variance26090.307
MonotonicityNot monotonic
2023-12-12T22:54:54.141717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4 3
 
8.8%
2 3
 
8.8%
1 2
 
5.9%
3 2
 
5.9%
0 2
 
5.9%
685 1
 
2.9%
22 1
 
2.9%
79 1
 
2.9%
207 1
 
2.9%
189 1
 
2.9%
Other values (17) 17
50.0%
ValueCountFrequency (%)
0 2
5.9%
1 2
5.9%
2 3
8.8%
3 2
5.9%
4 3
8.8%
5 1
 
2.9%
6 1
 
2.9%
8 1
 
2.9%
14 1
 
2.9%
22 1
 
2.9%
ValueCountFrequency (%)
685 1
2.9%
582 1
2.9%
320 1
2.9%
228 1
2.9%
223 1
2.9%
218 1
2.9%
207 1
2.9%
189 1
2.9%
182 1
2.9%
178 1
2.9%

소방
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.294118
Minimum0
Maximum259
Zeros3
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:54.285193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q353
95-th percentile130.95
Maximum259
Range259
Interquartile range (IQR)50

Descriptive statistics

Standard deviation55.597324
Coefficient of variation (CV)1.5752575
Kurtosis8.7891464
Mean35.294118
Median Absolute Deviation (MAD)10
Skewness2.7920045
Sum1200
Variance3091.0624
MonotonicityNot monotonic
2023-12-12T22:54:54.403587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 5
 
14.7%
1 3
 
8.8%
0 3
 
8.8%
4 2
 
5.9%
5 2
 
5.9%
259 1
 
2.9%
37 1
 
2.9%
30 1
 
2.9%
59 1
 
2.9%
46 1
 
2.9%
Other values (14) 14
41.2%
ValueCountFrequency (%)
0 3
8.8%
1 3
8.8%
2 1
 
2.9%
3 5
14.7%
4 2
 
5.9%
5 2
 
5.9%
7 1
 
2.9%
13 1
 
2.9%
16 1
 
2.9%
27 1
 
2.9%
ValueCountFrequency (%)
259 1
2.9%
194 1
2.9%
97 1
2.9%
67 1
2.9%
60 1
2.9%
59 1
2.9%
58 1
2.9%
56 1
2.9%
54 1
2.9%
50 1
2.9%

교육직
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.11765
Minimum27
Maximum1549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:54.533600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile80.2
Q1181.5
median319.5
Q3407.5
95-th percentile970.35
Maximum1549
Range1522
Interquartile range (IQR)226

Descriptive statistics

Standard deviation326.90595
Coefficient of variation (CV)0.87850161
Kurtosis6.6321361
Mean372.11765
Median Absolute Deviation (MAD)117
Skewness2.416795
Sum12652
Variance106867.5
MonotonicityNot monotonic
2023-12-12T22:54:54.683727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
136 2
 
5.9%
508 1
 
2.9%
281 1
 
2.9%
342 1
 
2.9%
331 1
 
2.9%
308 1
 
2.9%
414 1
 
2.9%
413 1
 
2.9%
640 1
 
2.9%
334 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
27 1
2.9%
49 1
2.9%
97 1
2.9%
110 1
2.9%
118 1
2.9%
136 2
5.9%
151 1
2.9%
180 1
2.9%
186 1
2.9%
219 1
2.9%
ValueCountFrequency (%)
1549 1
2.9%
1400 1
2.9%
739 1
2.9%
667 1
2.9%
640 1
2.9%
508 1
2.9%
482 1
2.9%
414 1
2.9%
413 1
2.9%
391 1
2.9%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5882353
Minimum0
Maximum101
Zeros12
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:54.802451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34.5
95-th percentile21.1
Maximum101
Range101
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation18.131034
Coefficient of variation (CV)2.752032
Kurtosis23.771707
Mean6.5882353
Median Absolute Deviation (MAD)1
Skewness4.6871021
Sum224
Variance328.7344
MonotonicityNot monotonic
2023-12-12T22:54:54.927808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12
35.3%
1 9
26.5%
3 3
 
8.8%
11 2
 
5.9%
8 2
 
5.9%
101 1
 
2.9%
10 1
 
2.9%
12 1
 
2.9%
38 1
 
2.9%
5 1
 
2.9%
ValueCountFrequency (%)
0 12
35.3%
1 9
26.5%
2 1
 
2.9%
3 3
 
8.8%
5 1
 
2.9%
8 2
 
5.9%
10 1
 
2.9%
11 2
 
5.9%
12 1
 
2.9%
38 1
 
2.9%
ValueCountFrequency (%)
101 1
 
2.9%
38 1
 
2.9%
12 1
 
2.9%
11 2
 
5.9%
10 1
 
2.9%
8 2
 
5.9%
5 1
 
2.9%
3 3
 
8.8%
2 1
 
2.9%
1 9
26.5%

기능직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3823529
Minimum0
Maximum39
Zeros20
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:55.023394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile23.05
Maximum39
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.0621247
Coefficient of variation (CV)2.0678674
Kurtosis6.2679432
Mean4.3823529
Median Absolute Deviation (MAD)0
Skewness2.5108825
Sum149
Variance82.122103
MonotonicityNot monotonic
2023-12-12T22:54:55.147305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 20
58.8%
2 3
 
8.8%
1 3
 
8.8%
39 1
 
2.9%
25 1
 
2.9%
22 1
 
2.9%
7 1
 
2.9%
18 1
 
2.9%
8 1
 
2.9%
16 1
 
2.9%
ValueCountFrequency (%)
0 20
58.8%
1 3
 
8.8%
2 3
 
8.8%
5 1
 
2.9%
7 1
 
2.9%
8 1
 
2.9%
16 1
 
2.9%
18 1
 
2.9%
22 1
 
2.9%
25 1
 
2.9%
ValueCountFrequency (%)
39 1
 
2.9%
25 1
 
2.9%
22 1
 
2.9%
18 1
 
2.9%
16 1
 
2.9%
8 1
 
2.9%
7 1
 
2.9%
5 1
 
2.9%
2 3
8.8%
1 3
8.8%

공안직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.147059
Minimum0
Maximum309
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:55.275214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median6
Q321.5
95-th percentile250.75
Maximum309
Range309
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation81.745404
Coefficient of variation (CV)2.0361493
Kurtosis5.0717393
Mean40.147059
Median Absolute Deviation (MAD)5.5
Skewness2.4574535
Sum1365
Variance6682.3111
MonotonicityNot monotonic
2023-12-12T22:54:55.386813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 5
14.7%
1 4
 
11.8%
2 3
 
8.8%
4 2
 
5.9%
3 2
 
5.9%
7 2
 
5.9%
11 2
 
5.9%
280 1
 
2.9%
9 1
 
2.9%
37 1
 
2.9%
Other values (11) 11
32.4%
ValueCountFrequency (%)
0 5
14.7%
1 4
11.8%
2 3
8.8%
3 2
 
5.9%
4 2
 
5.9%
5 1
 
2.9%
7 2
 
5.9%
9 1
 
2.9%
11 2
 
5.9%
13 1
 
2.9%
ValueCountFrequency (%)
309 1
2.9%
280 1
2.9%
235 1
2.9%
163 1
2.9%
113 1
2.9%
47 1
2.9%
40 1
2.9%
37 1
2.9%
23 1
2.9%
17 1
2.9%

군무원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
0
32 
1286
 
1
388
 
1

Length

Max length4
Median length1
Mean length1.1470588
Min length1

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st row1286
2nd row388
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
94.1%
1286 1
 
2.9%
388 1
 
2.9%

Length

2023-12-12T22:54:55.517947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:54:55.631827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
94.1%
1286 1
 
2.9%
388 1
 
2.9%

연구직
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.588235
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:55.729421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.65
Q14.25
median7
Q315.5
95-th percentile25.75
Maximum63
Range62
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation11.775984
Coefficient of variation (CV)1.0162017
Kurtosis10.35079
Mean11.588235
Median Absolute Deviation (MAD)4.5
Skewness2.7247675
Sum394
Variance138.6738
MonotonicityNot monotonic
2023-12-12T22:54:55.854573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 4
11.8%
14 3
 
8.8%
3 3
 
8.8%
6 3
 
8.8%
11 3
 
8.8%
1 2
 
5.9%
24 2
 
5.9%
2 2
 
5.9%
4 2
 
5.9%
7 2
 
5.9%
Other values (8) 8
23.5%
ValueCountFrequency (%)
1 2
5.9%
2 2
5.9%
3 3
8.8%
4 2
5.9%
5 4
11.8%
6 3
8.8%
7 2
5.9%
11 3
8.8%
12 1
 
2.9%
14 3
8.8%
ValueCountFrequency (%)
63 1
 
2.9%
29 1
 
2.9%
24 2
5.9%
21 1
 
2.9%
20 1
 
2.9%
18 1
 
2.9%
17 1
 
2.9%
16 1
 
2.9%
14 3
8.8%
12 1
 
2.9%

지도직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2941176
Minimum0
Maximum30
Zeros6
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:55.961649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile23.7
Maximum30
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.3431315
Coefficient of variation (CV)1.3255443
Kurtosis1.2857419
Mean6.2941176
Median Absolute Deviation (MAD)2
Skewness1.5230812
Sum214
Variance69.607843
MonotonicityNot monotonic
2023-12-12T22:54:56.068694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 10
29.4%
0 6
17.6%
4 3
 
8.8%
2 2
 
5.9%
6 2
 
5.9%
8 2
 
5.9%
20 1
 
2.9%
17 1
 
2.9%
11 1
 
2.9%
25 1
 
2.9%
Other values (5) 5
14.7%
ValueCountFrequency (%)
0 6
17.6%
1 10
29.4%
2 2
 
5.9%
3 1
 
2.9%
4 3
 
8.8%
6 2
 
5.9%
8 2
 
5.9%
11 1
 
2.9%
12 1
 
2.9%
17 1
 
2.9%
ValueCountFrequency (%)
30 1
2.9%
25 1
2.9%
23 1
2.9%
20 1
2.9%
19 1
2.9%
17 1
2.9%
12 1
2.9%
11 1
2.9%
8 2
5.9%
6 2
5.9%

계약직
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.352941
Minimum15
Maximum718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:56.174669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20.3
Q134.25
median52.5
Q381.25
95-th percentile386.95
Maximum718
Range703
Interquartile range (IQR)47

Descriptive statistics

Standard deviation150.91255
Coefficient of variation (CV)1.5189541
Kurtosis11.236136
Mean99.352941
Median Absolute Deviation (MAD)22.5
Skewness3.3548464
Sum3378
Variance22774.599
MonotonicityNot monotonic
2023-12-12T22:54:56.288943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
30 2
 
5.9%
39 2
 
5.9%
718 1
 
2.9%
93 1
 
2.9%
44 1
 
2.9%
22 1
 
2.9%
68 1
 
2.9%
50 1
 
2.9%
15 1
 
2.9%
84 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
15 1
2.9%
19 1
2.9%
21 1
2.9%
22 1
2.9%
23 1
2.9%
30 2
5.9%
32 1
2.9%
34 1
2.9%
35 1
2.9%
39 2
5.9%
ValueCountFrequency (%)
718 1
2.9%
593 1
2.9%
276 1
2.9%
200 1
2.9%
110 1
2.9%
99 1
2.9%
93 1
2.9%
84 1
2.9%
82 1
2.9%
79 1
2.9%

공중보건의
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.676471
Minimum0
Maximum220
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:56.404939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q321
95-th percentile161.05
Maximum220
Range220
Interquartile range (IQR)21

Descriptive statistics

Standard deviation63.478806
Coefficient of variation (CV)1.7307774
Kurtosis1.4582127
Mean36.676471
Median Absolute Deviation (MAD)1.5
Skewness1.6429107
Sum1247
Variance4029.5588
MonotonicityNot monotonic
2023-12-12T22:54:56.504905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 17
50.0%
7 2
 
5.9%
21 2
 
5.9%
4 1
 
2.9%
87 1
 
2.9%
189 1
 
2.9%
119 1
 
2.9%
126 1
 
2.9%
220 1
 
2.9%
137 1
 
2.9%
Other values (6) 6
 
17.6%
ValueCountFrequency (%)
0 17
50.0%
3 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
7 2
 
5.9%
13 1
 
2.9%
18 1
 
2.9%
21 2
 
5.9%
87 1
 
2.9%
119 1
 
2.9%
ValueCountFrequency (%)
220 1
2.9%
189 1
2.9%
146 1
2.9%
137 1
2.9%
126 1
2.9%
124 1
2.9%
119 1
2.9%
87 1
2.9%
21 2
5.9%
18 1
2.9%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.32353
Minimum32
Maximum1639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T22:54:56.627989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile37.9
Q168
median109
Q3171.75
95-th percentile1017.55
Maximum1639
Range1607
Interquartile range (IQR)103.75

Descriptive statistics

Standard deviation372.74191
Coefficient of variation (CV)1.6113445
Kurtosis8.4956099
Mean231.32353
Median Absolute Deviation (MAD)52.5
Skewness2.9776576
Sum7865
Variance138936.53
MonotonicityNot monotonic
2023-12-12T22:54:56.805210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
44 2
 
5.9%
140 2
 
5.9%
1639 1
 
2.9%
113 1
 
2.9%
84 1
 
2.9%
105 1
 
2.9%
143 1
 
2.9%
57 1
 
2.9%
80 1
 
2.9%
127 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
32 1
2.9%
34 1
2.9%
40 1
2.9%
43 1
2.9%
44 2
5.9%
48 1
2.9%
57 1
2.9%
67 1
2.9%
71 1
2.9%
80 1
2.9%
ValueCountFrequency (%)
1639 1
2.9%
1442 1
2.9%
789 1
2.9%
752 1
2.9%
263 1
2.9%
207 1
2.9%
204 1
2.9%
177 1
2.9%
175 1
2.9%
162 1
2.9%

Interactions

2023-12-12T22:54:49.462095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:27.824719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.137859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.648772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.461420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.854785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.314554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.621759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.232588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.474714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.168767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.675960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.552948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.033399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.892674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.562383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:27.882587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.230579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.739880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.534716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.952105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.382809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.705344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.312106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.559991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.255246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.753219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.635879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.135313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.005893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.670573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:27.957970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.309091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.832720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.620649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.076563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.464111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.788429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.408438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.633770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.373425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.843116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.729802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.218653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.129291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.784305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.064145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.416346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.970126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.700716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.190555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.543621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.881917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.502027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.728757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.494261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.954707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.832987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.313089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.235261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.905865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.139740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.504761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.075685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.783595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.283857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.636845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.953828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.578048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.817381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.590164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:43.049961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.917635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.405459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.324900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:50.019785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.227579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.609342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.188334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.884153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.373611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.720925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.041886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.651916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.937370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.679955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:43.150568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.019618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.525128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.437119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:50.142375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.306965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.720543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.288286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.976823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.455310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.796782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.123592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.740010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.065769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.767955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:43.250384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.128854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.667363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.532946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:50.261597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.389998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.823973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.402365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.067123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.549934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.907606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.216246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.838570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.171309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.880527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:43.352214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.247066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.796504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.647733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:50.359781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.463797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.920558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.488120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.148119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.642215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.994133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.326368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.938898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.265006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.975165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:43.789916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.337479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:46.933842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.763941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:50.860139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.560623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.028167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.592577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.237972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.746974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.104157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.710874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.017924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.382294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.077114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:43.893500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.445641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.077440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.872499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:50.974747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.642659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.135596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:31.690315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.343946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.826356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.181853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.790851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.091778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.492849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.167968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.002442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.545922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.202543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.973643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:51.082424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.736870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.225796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.103646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.452943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:34.931856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.259532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.874891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.168993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.614260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.277768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.114634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.656242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.320913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.074140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:51.227084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.812791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.320254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.193675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.560777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.020779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.353612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:37.965742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.242175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.727482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.388812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.205359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.749683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.457724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.175636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:51.356856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:28.925006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.428490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.298173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.658720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.129651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.448866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.058584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.324869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:40.834028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.482106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.316771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.842602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.615863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.293973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:51.471440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:29.042136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:30.526370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:32.371853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:33.753428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:35.209833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:36.534884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:38.142552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:39.390958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:41.039309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:42.587014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:44.406159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:45.933911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.739032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:49.371996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:54:57.238889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역남여구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
지역1.0000.0000.0000.3250.3300.0000.0000.0000.6760.0640.4280.3310.3990.0000.4810.3740.0000.859
남여구분0.0001.0000.7000.0000.1710.6110.7950.9780.0000.4510.2590.2510.0000.7210.5330.3940.5970.000
0.0000.7001.0000.7330.8020.8960.8060.9180.7840.8210.8070.8170.9820.5040.1060.9680.6510.835
정무직0.3250.0000.7331.0000.8160.7890.6180.6680.4520.8880.8010.6900.7880.2100.0000.8600.0000.753
별정직0.3300.1710.8020.8161.0000.9090.8390.7780.5530.8440.8360.6461.0000.2490.0000.9110.0000.976
일반직0.0000.6110.8960.7890.9091.0000.9480.8450.5890.8650.9170.7071.0000.5160.3390.8910.5830.863
경찰0.0000.7950.8060.6180.8390.9481.0000.9110.2790.9270.8980.7070.7180.5170.5940.7410.6430.738
소방0.0000.9780.9180.6680.7780.8450.9111.0000.5380.9340.5620.8010.9170.7670.7630.9320.8180.670
교육직0.6760.0000.7840.4520.5530.5890.2790.5381.0000.5110.4170.8270.8490.0000.0000.8450.0000.619
법관검사0.0640.4510.8210.8880.8440.8650.9270.9340.5111.0000.7410.8520.6490.3420.0000.9020.2680.801
기능직0.4280.2590.8070.8010.8360.9170.8980.5620.4170.7411.0000.6141.0000.0000.2770.7170.5850.854
공안직0.3310.2510.8170.6900.6460.7070.7070.8010.8270.8520.6141.0000.8490.4630.0000.8910.0000.699
군무원0.3990.0000.9820.7881.0001.0000.7180.9170.8490.6491.0000.8491.0000.4470.0001.0000.0001.000
연구직0.0000.7210.5040.2100.2490.5160.5170.7670.0000.3420.0000.4630.4471.0000.7880.3750.8740.262
지도직0.4810.5330.1060.0000.0000.3390.5940.7630.0000.0000.2770.0000.0000.7881.0000.0000.9800.000
계약직0.3740.3940.9680.8600.9110.8910.7410.9320.8450.9020.7170.8911.0000.3750.0001.0000.0000.900
공중보건의0.0000.5970.6510.0000.0000.5830.6430.8180.0000.2680.5850.0000.0000.8740.9800.0001.0000.000
기타0.8590.0000.8350.7530.9760.8630.7380.6700.6190.8010.8540.6991.0000.2620.0000.9000.0001.000
2023-12-12T22:54:57.411397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남여구분군무원
남여구분1.0000.000
군무원0.0001.000
2023-12-12T22:54:57.528004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직일반직경찰소방교육직법관검사기능직공안직연구직지도직계약직공중보건의기타남여구분군무원
1.0000.3070.7090.9870.7460.7810.6780.7080.4850.5860.6110.3970.7460.5220.7060.4950.793
정무직0.3071.0000.5850.3520.3320.386-0.0790.3060.1570.2830.499-0.2480.5860.2670.3820.0000.833
별정직0.7090.5851.0000.7180.7210.6780.2290.5390.3370.5910.558-0.0530.8800.5190.6570.1900.967
일반직0.9870.3520.7181.0000.7430.7840.6140.6760.4800.5740.6370.3880.7610.5410.7010.6020.933
경찰0.7460.3320.7210.7431.0000.8980.1600.5560.2780.6820.6510.2940.6920.8250.4430.7920.592
소방0.7810.3860.6780.7840.8981.0000.2460.5010.2620.5100.7460.4210.6920.8430.4090.8110.619
교육직0.678-0.0790.2290.6140.1600.2461.0000.4120.4510.2000.1760.3520.269-0.0500.4970.0000.513
법관검사0.7080.3060.5390.6760.5560.5010.4121.0000.3380.7430.3710.0160.5710.2420.6100.2900.662
기능직0.4850.1570.3370.4800.2780.2620.4510.3381.0000.3420.126-0.1390.2790.0480.3010.2450.933
공안직0.5860.2830.5910.5740.6820.5100.2000.7430.3421.0000.423-0.0910.6020.3000.5730.1550.513
연구직0.6110.4990.5580.6370.6510.7460.1760.3710.1260.4231.0000.2900.5820.6010.4800.4950.185
지도직0.397-0.248-0.0530.3880.2940.4210.3520.016-0.139-0.0910.2901.0000.0300.549-0.0150.4540.000
계약직0.7460.5860.8800.7610.6920.6920.2690.5710.2790.6020.5820.0301.0000.4970.6970.2570.950
공중보건의0.5220.2670.5190.5410.8250.843-0.0500.2420.0480.3000.6010.5490.4971.0000.0940.4010.000
기타0.7060.3820.6570.7010.4430.4090.4970.6100.3010.5730.480-0.0150.6970.0941.0000.0000.967
남여구분0.4950.0000.1900.6020.7920.8110.0000.2900.2450.1550.4950.4540.2570.4010.0001.0000.000
군무원0.7930.8330.9670.9330.5920.6190.5130.6620.9330.5130.1850.0000.9500.0000.9670.0001.000

Missing values

2023-12-12T22:54:51.651341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:54:51.918013image/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.

Sample

지역남여구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
0서울849655295247768525964010139280128618071841639
1서울528861601169377140010254038811059301442
2부산2063041881320673631122470211757207
3부산137802041243667372031520204
4대구180212578018256219802350112997177
5대구7590224043332302305158088
6인천14061256452236022810140202722194
7인천88607282813911011051390140
8광주10400134509927136120113051533128
9광주4090315650136101301023071
지역남여구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
24경북1951059842185830838406305022057
25경북896003702241402003815080
26경남1867024728228974132030172393126113
27경남10880239725508101078300127
28전북16901266711894629810110631984119162
29전북7530328513307001016434099
30전남1841022826207593460167011127918967
31전남874022911348215105435044
32제주580032167930118004073512148
33제주26000914011000001119034