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

Description공무원의 직종별(정무직,별정직,일반직,경찰,소방,교육직,연구직 등), 지역별(서울, 부산,대전, 대구, 제주 등) 공무원연금 가입자 수 데이터입니다.
URLhttps://www.data.go.kr/data/15053030/fileData.do

Alerts

is highly overall correlated with 별정직 and 9 other fieldsHigh correlation
정무직 is highly overall correlated with 별정직 and 4 other fieldsHigh correlation
별정직 is highly overall correlated with and 10 other fieldsHigh correlation
일반직 is highly overall correlated with and 6 other fieldsHigh correlation
경찰 is highly overall correlated with and 10 other fieldsHigh correlation
소방 is highly overall correlated with and 7 other fieldsHigh correlation
교육직 is highly overall correlated with and 3 other fieldsHigh correlation
법관검사 is highly overall correlated with and 7 other fieldsHigh correlation
기능직 is highly overall correlated with 군무원High correlation
공안직 is highly overall correlated with and 6 other fieldsHigh correlation
연구직 is highly overall correlated with 법관검사 and 3 other fieldsHigh correlation
계약직 is highly overall correlated with and 8 other fieldsHigh correlation
공중보건의 is highly overall correlated with 별정직 and 2 other fieldsHigh correlation
기타 is highly overall correlated with and 9 other fieldsHigh correlation
남녀구분 is highly overall correlated with 경찰 and 2 other fieldsHigh correlation
군무원 is highly overall correlated with and 12 other fieldsHigh correlation
군무원 is highly imbalanced (75.9%)Imbalance
has unique valuesUnique
일반직 has unique valuesUnique
경찰 has unique valuesUnique
교육직 has unique valuesUnique
공안직 has unique valuesUnique
기타 has unique valuesUnique
정무직 has 11 (32.4%) zerosZeros
법관검사 has 2 (5.9%) zerosZeros
기능직 has 22 (64.7%) zerosZeros
공중보건의 has 17 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-12 11:04:17.752609
Analysis finished2023-12-12 11:04:52.316464
Duration34.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T20:04:52.492887image/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%
2023-12-12T20:04:52.932749image/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%

남녀구분
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

2023-12-12T20:04:53.136863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:53.280076image/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%
Mean37676.294
Minimum9008
Maximum134187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:53.455551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9008
5-th percentile11146.35
Q122336.5
median29300
Q335864.5
95-th percentile118981.7
Maximum134187
Range125179
Interquartile range (IQR)13528

Descriptive statistics

Standard deviation32191.757
Coefficient of variation (CV)0.85443003
Kurtosis3.9138328
Mean37676.294
Median Absolute Deviation (MAD)6939
Skewness2.2135217
Sum1280994
Variance1.0363092 × 109
MonotonicityNot monotonic
2023-12-12T20:04:53.652915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
126471 1
 
2.9%
37560 1
 
2.9%
23696 1
 
2.9%
22075 1
 
2.9%
29853 1
 
2.9%
26842 1
 
2.9%
36017 1
 
2.9%
29802 1
 
2.9%
38521 1
 
2.9%
32424 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
9008 1
2.9%
10926 1
2.9%
11265 1
2.9%
13074 1
2.9%
14392 1
2.9%
18141 1
2.9%
19469 1
2.9%
22075 1
2.9%
22139 1
2.9%
22929 1
2.9%
ValueCountFrequency (%)
134187 1
2.9%
126471 1
2.9%
114949 1
2.9%
108706 1
2.9%
39996 1
2.9%
38521 1
2.9%
38358 1
2.9%
37560 1
2.9%
36017 1
2.9%
35407 1
2.9%

정무직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0882353
Minimum0
Maximum61
Zeros11
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:53.827455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile26.85
Maximum61
Range61
Interquartile range (IQR)2

Descriptive statistics

Standard deviation12.884963
Coefficient of variation (CV)2.5323049
Kurtosis13.695867
Mean5.0882353
Median Absolute Deviation (MAD)1
Skewness3.7273616
Sum173
Variance166.02228
MonotonicityNot monotonic
2023-12-12T20:04:53.991788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 11
32.4%
0 11
32.4%
1 4
 
11.8%
3 3
 
8.8%
7 2
 
5.9%
61 1
 
2.9%
47 1
 
2.9%
16 1
 
2.9%
ValueCountFrequency (%)
0 11
32.4%
1 4
 
11.8%
2 11
32.4%
3 3
 
8.8%
7 2
 
5.9%
16 1
 
2.9%
47 1
 
2.9%
61 1
 
2.9%
ValueCountFrequency (%)
61 1
 
2.9%
47 1
 
2.9%
16 1
 
2.9%
7 2
 
5.9%
3 3
 
8.8%
2 11
32.4%
1 4
 
11.8%
0 11
32.4%

별정직
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.323529
Minimum1
Maximum517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:54.189264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.65
Q17.5
median21
Q336
95-th percentile183.05
Maximum517
Range516
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation98.406923
Coefficient of variation (CV)2.0364184
Kurtosis17.042358
Mean48.323529
Median Absolute Deviation (MAD)14.5
Skewness4.0056309
Sum1643
Variance9683.9225
MonotonicityNot monotonic
2023-12-12T20:04:54.400187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5 3
 
8.8%
49 2
 
5.9%
36 2
 
5.9%
7 2
 
5.9%
16 2
 
5.9%
517 1
 
2.9%
119 1
 
2.9%
18 1
 
2.9%
1 1
 
2.9%
6 1
 
2.9%
Other values (18) 18
52.9%
ValueCountFrequency (%)
1 1
 
2.9%
3 1
 
2.9%
4 1
 
2.9%
5 3
8.8%
6 1
 
2.9%
7 2
5.9%
9 1
 
2.9%
12 1
 
2.9%
15 1
 
2.9%
16 2
5.9%
ValueCountFrequency (%)
517 1
2.9%
302 1
2.9%
119 1
2.9%
84 1
2.9%
51 1
2.9%
49 2
5.9%
37 1
2.9%
36 2
5.9%
34 1
2.9%
33 1
2.9%

일반직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15157.971
Minimum3555
Maximum47319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:54.664826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3555
5-th percentile4019.65
Q110178.25
median12127
Q314975.25
95-th percentile42000.35
Maximum47319
Range43764
Interquartile range (IQR)4797

Descriptive statistics

Standard deviation10905.455
Coefficient of variation (CV)0.71945351
Kurtosis3.6616187
Mean15157.971
Median Absolute Deviation (MAD)2745
Skewness2.0431533
Sum515371
Variance1.1892895 × 108
MonotonicityNot monotonic
2023-12-12T20:04:54.863436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
36955 1
 
2.9%
14996 1
 
2.9%
10045 1
 
2.9%
9357 1
 
2.9%
12271 1
 
2.9%
11675 1
 
2.9%
16441 1
 
2.9%
14847 1
 
2.9%
16321 1
 
2.9%
13496 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
3555 1
2.9%
3759 1
2.9%
4160 1
2.9%
4680 1
2.9%
8197 1
2.9%
8488 1
2.9%
9357 1
2.9%
9423 1
2.9%
10045 1
2.9%
10578 1
2.9%
ValueCountFrequency (%)
47319 1
2.9%
46707 1
2.9%
39466 1
2.9%
36955 1
2.9%
18681 1
2.9%
16441 1
2.9%
16321 1
2.9%
15754 1
2.9%
14996 1
2.9%
14913 1
2.9%

경찰
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4228.2647
Minimum119
Maximum26975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:55.078650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile490.85
Q1822
median2682.5
Q35229
95-th percentile13694.15
Maximum26975
Range26856
Interquartile range (IQR)4407

Descriptive statistics

Standard deviation5785.7072
Coefficient of variation (CV)1.3683408
Kurtosis8.8420916
Mean4228.2647
Median Absolute Deviation (MAD)2043.5
Skewness2.8414125
Sum143761
Variance33474407
MonotonicityNot monotonic
2023-12-12T20:04:55.292208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
26975 1
 
2.9%
6696 1
 
2.9%
3457 1
 
2.9%
574 1
 
2.9%
5284 1
 
2.9%
888 1
 
2.9%
5628 1
 
2.9%
911 1
 
2.9%
1095 1
 
2.9%
5064 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
119 1
2.9%
446 1
2.9%
515 1
2.9%
520 1
2.9%
566 1
2.9%
574 1
2.9%
704 1
2.9%
756 1
2.9%
800 1
2.9%
888 1
2.9%
ValueCountFrequency (%)
26975 1
2.9%
22137 1
2.9%
9148 1
2.9%
8711 1
2.9%
7110 1
2.9%
6696 1
2.9%
6115 1
2.9%
5628 1
2.9%
5284 1
2.9%
5064 1
2.9%

소방
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1886
Minimum82
Maximum9419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:55.475037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile98.5
Q1314.75
median943.5
Q33017.25
95-th percentile5554.1
Maximum9419
Range9337
Interquartile range (IQR)2702.5

Descriptive statistics

Standard deviation2175.3321
Coefficient of variation (CV)1.1534105
Kurtosis3.2967742
Mean1886
Median Absolute Deviation (MAD)756
Skewness1.7207181
Sum64124
Variance4732069.8
MonotonicityNot monotonic
2023-12-12T20:04:55.667817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
135 2
 
5.9%
6689 1
 
2.9%
378 1
 
2.9%
3479 1
 
2.9%
597 1
 
2.9%
4943 1
 
2.9%
557 1
 
2.9%
4749 1
 
2.9%
429 1
 
2.9%
332 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
82 1
2.9%
92 1
2.9%
102 1
2.9%
135 2
5.9%
240 1
2.9%
276 1
2.9%
280 1
2.9%
309 1
2.9%
332 1
2.9%
378 1
2.9%
ValueCountFrequency (%)
9419 1
2.9%
6689 1
2.9%
4943 1
2.9%
4749 1
2.9%
3921 1
2.9%
3822 1
2.9%
3479 1
2.9%
3302 1
2.9%
3035 1
2.9%
2964 1
2.9%

교육직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11138.5
Minimum1281
Maximum73117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:55.834795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1281
5-th percentile2160.45
Q15317.25
median7727
Q311974
95-th percentile26723.5
Maximum73117
Range71836
Interquartile range (IQR)6656.75

Descriptive statistics

Standard deviation13015.882
Coefficient of variation (CV)1.1685489
Kurtosis16.304534
Mean11138.5
Median Absolute Deviation (MAD)3443
Skewness3.7549362
Sum378709
Variance1.6941318 × 108
MonotonicityNot monotonic
2023-12-12T20:04:56.012826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9205 1
 
2.9%
8467 1
 
2.9%
5513 1
 
2.9%
10046 1
 
2.9%
6756 1
 
2.9%
12162 1
 
2.9%
6708 1
 
2.9%
12185 1
 
2.9%
18907 1
 
2.9%
6156 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1281 1
2.9%
1970 1
2.9%
2263 1
2.9%
3457 1
2.9%
3538 1
2.9%
4018 1
2.9%
4217 1
2.9%
4899 1
2.9%
5252 1
2.9%
5513 1
2.9%
ValueCountFrequency (%)
73117 1
2.9%
39704 1
2.9%
19734 1
2.9%
18907 1
2.9%
15212 1
2.9%
14812 1
2.9%
13259 1
2.9%
12185 1
2.9%
12162 1
2.9%
11410 1
2.9%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.67647
Minimum0
Maximum1493
Zeros2
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:56.183883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.1
Q122.25
median51.5
Q3155.75
95-th percentile647.3
Maximum1493
Range1493
Interquartile range (IQR)133.5

Descriptive statistics

Standard deviation288.67143
Coefficient of variation (CV)1.807852
Kurtosis14.158361
Mean159.67647
Median Absolute Deviation (MAD)37
Skewness3.5200582
Sum5429
Variance83331.195
MonotonicityNot monotonic
2023-12-12T20:04:56.361520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
22 2
 
5.9%
21 2
 
5.9%
0 2
 
5.9%
16 1
 
2.9%
64 1
 
2.9%
34 1
 
2.9%
23 1
 
2.9%
33 1
 
2.9%
1493 1
 
2.9%
55 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
0 2
5.9%
14 1
2.9%
15 1
2.9%
16 1
2.9%
21 2
5.9%
22 2
5.9%
23 1
2.9%
29 1
2.9%
31 1
2.9%
33 1
2.9%
ValueCountFrequency (%)
1493 1
2.9%
763 1
2.9%
585 1
2.9%
347 1
2.9%
307 1
2.9%
220 1
2.9%
219 1
2.9%
213 1
2.9%
170 1
2.9%
113 1
2.9%

기능직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8823529
Minimum0
Maximum17
Zeros22
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:56.558051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.75
95-th percentile8.4
Maximum17
Range17
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation3.7153629
Coefficient of variation (CV)1.9737865
Kurtosis8.2544143
Mean1.8823529
Median Absolute Deviation (MAD)0
Skewness2.7125547
Sum64
Variance13.803922
MonotonicityNot monotonic
2023-12-12T20:04:56.733075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 22
64.7%
4 2
 
5.9%
1 2
 
5.9%
3 2
 
5.9%
17 1
 
2.9%
11 1
 
2.9%
5 1
 
2.9%
7 1
 
2.9%
2 1
 
2.9%
6 1
 
2.9%
ValueCountFrequency (%)
0 22
64.7%
1 2
 
5.9%
2 1
 
2.9%
3 2
 
5.9%
4 2
 
5.9%
5 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
11 1
 
2.9%
17 1
 
2.9%
ValueCountFrequency (%)
17 1
 
2.9%
11 1
 
2.9%
7 1
 
2.9%
6 1
 
2.9%
5 1
 
2.9%
4 2
 
5.9%
3 2
 
5.9%
2 1
 
2.9%
1 2
 
5.9%
0 22
64.7%

공안직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1107.7059
Minimum1
Maximum7198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:56.904945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62.25
Q1148.75
median315
Q3928.75
95-th percentile4677.45
Maximum7198
Range7197
Interquartile range (IQR)780

Descriptive statistics

Standard deviation1727.5188
Coefficient of variation (CV)1.5595465
Kurtosis4.7112693
Mean1107.7059
Median Absolute Deviation (MAD)222
Skewness2.2257483
Sum37662
Variance2984321.3
MonotonicityNot monotonic
2023-12-12T20:04:57.113026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4136 1
 
2.9%
400 1
 
2.9%
319 1
 
2.9%
184 1
 
2.9%
185 1
 
2.9%
103 1
 
2.9%
164 1
 
2.9%
92 1
 
2.9%
222 1
 
2.9%
311 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1 1
2.9%
7 1
2.9%
92 1
2.9%
94 1
2.9%
103 1
2.9%
121 1
2.9%
131 1
2.9%
142 1
2.9%
147 1
2.9%
154 1
2.9%
ValueCountFrequency (%)
7198 1
2.9%
5683 1
2.9%
4136 1
2.9%
3127 1
2.9%
3126 1
2.9%
2971 1
2.9%
2185 1
2.9%
1213 1
2.9%
935 1
2.9%
910 1
2.9%

군무원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
0
32 
26010
 
1
8901
 
1

Length

Max length5
Median length1
Mean length1.2058824
Min length1

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st row26010
2nd row8901
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
94.1%
26010 1
 
2.9%
8901 1
 
2.9%

Length

2023-12-12T20:04:57.449711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:57.654271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
94.1%
26010 1
 
2.9%
8901 1
 
2.9%

연구직
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.76471
Minimum96
Maximum971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:57.831814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile97.95
Q1172.75
median249.5
Q3407.25
95-th percentile704.8
Maximum971
Range875
Interquartile range (IQR)234.5

Descriptive statistics

Standard deviation208.71919
Coefficient of variation (CV)0.6446632
Kurtosis1.6278881
Mean323.76471
Median Absolute Deviation (MAD)119
Skewness1.3049232
Sum11008
Variance43563.701
MonotonicityNot monotonic
2023-12-12T20:04:58.023636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
198 2
 
5.9%
96 2
 
5.9%
630 1
 
2.9%
296 1
 
2.9%
534 1
 
2.9%
681 1
 
2.9%
132 1
 
2.9%
196 1
 
2.9%
184 1
 
2.9%
413 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
96 2
5.9%
99 1
2.9%
113 1
2.9%
128 1
2.9%
132 1
2.9%
150 1
2.9%
154 1
2.9%
169 1
2.9%
184 1
2.9%
196 1
2.9%
ValueCountFrequency (%)
971 1
2.9%
749 1
2.9%
681 1
2.9%
630 1
2.9%
589 1
2.9%
534 1
2.9%
415 1
2.9%
413 1
2.9%
409 1
2.9%
402 1
2.9%

지도직
Real number (ℝ)

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.47059
Minimum9
Maximum364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:58.222511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12.3
Q122.25
median60
Q3257
95-th percentile343.8
Maximum364
Range355
Interquartile range (IQR)234.75

Descriptive statistics

Standard deviation130.7114
Coefficient of variation (CV)0.91106753
Kurtosis-1.6144054
Mean143.47059
Median Absolute Deviation (MAD)50
Skewness0.38432786
Sum4878
Variance17085.469
MonotonicityNot monotonic
2023-12-12T20:04:58.427057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
36 2
 
5.9%
9 1
 
2.9%
259 1
 
2.9%
205 1
 
2.9%
168 1
 
2.9%
291 1
 
2.9%
222 1
 
2.9%
364 1
 
2.9%
324 1
 
2.9%
239 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
9 1
2.9%
11 1
2.9%
13 1
2.9%
15 1
2.9%
16 1
2.9%
19 1
2.9%
20 1
2.9%
21 1
2.9%
22 1
2.9%
23 1
2.9%
ValueCountFrequency (%)
364 1
2.9%
349 1
2.9%
341 1
2.9%
324 1
2.9%
321 1
2.9%
303 1
2.9%
302 1
2.9%
291 1
2.9%
259 1
2.9%
251 1
2.9%

계약직
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467
Minimum92
Maximum3365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:58.652447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile124.65
Q1179.75
median254.5
Q3353.75
95-th percentile1755.15
Maximum3365
Range3273
Interquartile range (IQR)174

Descriptive statistics

Standard deviation729.26015
Coefficient of variation (CV)1.5615849
Kurtosis12.105542
Mean467
Median Absolute Deviation (MAD)85.5
Skewness3.5384168
Sum15878
Variance531820.36
MonotonicityNot monotonic
2023-12-12T20:04:58.878736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
182 2
 
5.9%
3365 1
 
2.9%
347 1
 
2.9%
177 1
 
2.9%
250 1
 
2.9%
179 1
 
2.9%
309 1
 
2.9%
144 1
 
2.9%
240 1
 
2.9%
3113 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
92 1
2.9%
111 1
2.9%
132 1
2.9%
144 1
2.9%
145 1
2.9%
162 1
2.9%
176 1
2.9%
177 1
2.9%
179 1
2.9%
182 2
5.9%
ValueCountFrequency (%)
3365 1
2.9%
3113 1
2.9%
1024 1
2.9%
857 1
2.9%
511 1
2.9%
388 1
2.9%
382 1
2.9%
372 1
2.9%
354 1
2.9%
353 1
2.9%

공중보건의
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.147059
Minimum0
Maximum624
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:59.564824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q375.75
95-th percentile444.2
Maximum624
Range624
Interquartile range (IQR)75.75

Descriptive statistics

Standard deviation172.30578
Coefficient of variation (CV)1.7378809
Kurtosis2.0473541
Mean99.147059
Median Absolute Deviation (MAD)5
Skewness1.7502652
Sum3371
Variance29689.281
MonotonicityNot monotonic
2023-12-12T20:04:59.769117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 17
50.0%
11 1
 
2.9%
296 1
 
2.9%
57 1
 
2.9%
624 1
 
2.9%
358 1
 
2.9%
405 1
 
2.9%
517 1
 
2.9%
370 1
 
2.9%
238 1
 
2.9%
Other values (8) 8
23.5%
ValueCountFrequency (%)
0 17
50.0%
10 1
 
2.9%
11 1
 
2.9%
18 1
 
2.9%
20 1
 
2.9%
23 1
 
2.9%
29 1
 
2.9%
50 1
 
2.9%
57 1
 
2.9%
82 1
 
2.9%
ValueCountFrequency (%)
624 1
2.9%
517 1
2.9%
405 1
2.9%
370 1
2.9%
358 1
2.9%
296 1
2.9%
263 1
2.9%
238 1
2.9%
82 1
2.9%
57 1
2.9%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1882.7059
Minimum267
Maximum10398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T20:04:59.967087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum267
5-th percentile298.4
Q1710.5
median927
Q32199.5
95-th percentile6976.75
Maximum10398
Range10131
Interquartile range (IQR)1489

Descriptive statistics

Standard deviation2309.2457
Coefficient of variation (CV)1.2265568
Kurtosis5.9379133
Mean1882.7059
Median Absolute Deviation (MAD)430
Skewness2.4190298
Sum64012
Variance5332615.5
MonotonicityNot monotonic
2023-12-12T20:05:00.198868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10398 1
 
2.9%
929 1
 
2.9%
809 1
 
2.9%
476 1
 
2.9%
774 1
 
2.9%
789 1
 
2.9%
694 1
 
2.9%
518 1
 
2.9%
800 1
 
2.9%
2099 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
267 1
2.9%
275 1
2.9%
311 1
2.9%
313 1
2.9%
398 1
2.9%
476 1
2.9%
518 1
2.9%
637 1
2.9%
694 1
2.9%
760 1
2.9%
ValueCountFrequency (%)
10398 1
2.9%
8046 1
2.9%
6401 1
2.9%
4281 1
2.9%
3624 1
2.9%
3335 1
2.9%
2846 1
2.9%
2500 1
2.9%
2233 1
2.9%
2099 1
2.9%

Interactions

2023-12-12T20:04:49.209012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:18.679626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:20.920130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:23.233234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.576996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.425903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.358877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.394542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.830626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.823529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.729514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.030081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.693791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.065960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.140707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:49.358010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:18.820573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.061725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:23.807299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.706689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.556073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.494020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.530268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.971313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.951866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.850477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.173676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.852596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.234544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.267930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:49.488163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:18.961732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.213813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:23.923378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.827863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.682879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.617352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.667093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.116318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.086625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.973255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.322392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.992890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.383723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.393076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:49.622290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.090041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.337476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.038643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.945869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.817206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.790087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.786255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.263413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.241810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:38.086960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.482282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:43.139038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.528602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.528119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:49.757074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.207853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.477655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.159652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.052717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.933429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.912045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.888747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.382365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.371475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:38.349501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.627018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:43.274108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.643342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.661485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:49.902915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.341536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.641216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.287858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.171309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.051016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.036695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.995401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.521316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.503160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:38.503109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.757481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:43.427277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.763307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.806684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:50.019668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.489008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.791898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.391269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.279686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.161963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.160361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:32.114023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.643014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.629200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:38.624297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:40.864670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:43.559910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:45.887275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.943456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:50.203411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.663394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:21.962205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.526063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.400126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.291834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.299493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:32.234327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.780689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.758634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:38.826390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:41.518209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:43.757962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.028975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.102806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:50.829837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.824207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:22.124240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.661984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.536386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.411233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.440870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:32.370184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:34.934108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:36.883279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:38.959702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:41.678780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:43.944735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.166272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.235828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:50.963535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:19.980590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:22.296374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.802234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.680409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.548361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.579939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:32.984596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.082393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.015969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:39.105245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:41.829963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:44.100502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.320619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.368884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:51.100324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:20.125570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:22.441854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:24.934752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.808792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.681210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.718064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.126947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.209525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.150133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:39.250759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:41.992912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:44.259542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.459797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.498208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:51.213544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:20.270543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:22.590938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.065149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:26.919113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.800941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.843183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.264649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.322048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.269204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:39.398214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.119439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:44.410186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.568625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.644902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:51.371898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:20.427323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:22.784810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.202951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.046424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:28.929329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:30.993434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.401041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.447314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.398391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:39.561240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.254516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:44.561600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.701072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.793304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:51.498937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:20.604420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:22.962307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.346486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.201948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.079072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.144639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.559748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.575094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.516768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:39.718449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.400745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:44.724295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:46.858380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:48.931768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:51.631218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:20.763434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:23.112558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:25.463645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:27.311249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:29.226009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:31.277209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:33.690501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:35.703612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:37.624119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:39.883435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:42.552629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:44.890482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:47.010093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:04:49.057620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:05:00.358817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남녀구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
구분1.0000.0000.7730.3300.3690.7680.0000.0000.0000.3650.4240.6010.3990.7370.6110.9330.1970.000
남녀구분0.0001.0000.0000.0000.0540.1160.9120.9750.4340.0000.2040.0000.0000.0000.5660.0000.5830.109
0.7730.0001.0000.7840.8560.8510.9170.7050.8010.9610.6360.7980.9820.7560.0000.8250.0000.824
정무직0.3300.0000.7841.0000.9970.8100.7980.7830.5760.8560.6920.8560.8150.6080.0000.8570.0000.855
별정직0.3690.0540.8560.9971.0000.8410.7850.7760.7260.9000.7660.8131.0000.7390.0000.8970.0000.894
일반직0.7680.1160.8510.8100.8411.0000.7610.7550.6920.8530.8000.9200.8510.6580.0000.7600.0000.944
경찰0.0000.9120.9170.7980.7850.7611.0000.9090.4890.9350.5440.8500.9300.6140.0000.6720.7080.804
소방0.0000.9750.7050.7830.7760.7550.9091.0000.4750.7770.4680.7720.7280.6670.7050.7840.9550.778
교육직0.0000.4340.8010.5760.7260.6920.4890.4751.0000.7720.5760.7470.6980.6570.0000.7520.0000.746
법관검사0.3650.0000.9610.8560.9000.8530.9350.7770.7721.0000.7500.8971.0000.7310.0000.8490.0000.946
기능직0.4240.2040.6360.6920.7660.8000.5440.4680.5760.7501.0000.7531.0000.6060.0000.5930.0000.894
공안직0.6010.0000.7980.8560.8130.9200.8500.7720.7470.8970.7531.0000.8080.5550.0000.7840.0000.958
군무원0.3990.0000.9820.8151.0000.8510.9300.7280.6981.0001.0000.8081.0000.8630.0000.6480.0001.000
연구직0.7370.0000.7560.6080.7390.6580.6140.6670.6570.7310.6060.5550.8631.0000.0000.7300.3930.783
지도직0.6110.5660.0000.0000.0000.0000.0000.7050.0000.0000.0000.0000.0000.0001.0000.0000.8540.000
계약직0.9330.0000.8250.8570.8970.7600.6720.7840.7520.8490.5930.7840.6480.7300.0001.0000.0000.782
공중보건의0.1970.5830.0000.0000.0000.0000.7080.9550.0000.0000.0000.0000.0000.3930.8540.0001.0000.000
기타0.0000.1090.8240.8550.8940.9440.8040.7780.7460.9460.8940.9581.0000.7830.0000.7820.0001.000
2023-12-12T20:05:00.632262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남녀구분군무원
남녀구분1.0000.000
군무원0.0001.000
2023-12-12T20:05:00.808001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직일반직경찰소방교육직법관검사기능직공안직연구직지도직계약직공중보건의기타남녀구분군무원
1.0000.1960.5410.9250.7260.5670.5480.6490.3270.6050.4950.3120.7210.2070.6420.0000.793
정무직0.1961.0000.8190.1750.4680.530-0.3460.4040.0590.4170.242-0.1660.6340.4530.5680.0000.813
별정직0.5410.8191.0000.5010.6970.647-0.1890.6140.1640.5910.265-0.1240.7880.5240.7340.0190.967
일반직0.9250.1750.5011.0000.5800.4960.5580.4620.3730.3980.3570.3320.7490.1530.5740.0000.772
경찰0.7260.4680.6970.5801.0000.917-0.0290.6070.2590.6020.3170.2910.5900.7240.5680.6870.644
소방0.5670.5300.6470.4960.9171.000-0.1700.4150.2420.4020.2200.4290.5650.8430.4740.7750.568
교육직0.548-0.346-0.1890.558-0.029-0.1701.0000.2450.0870.1870.3840.2670.169-0.4930.1580.5160.658
법관검사0.6490.4040.6140.4620.6070.4150.2451.0000.2170.9550.584-0.1810.5140.1410.7810.0000.950
기능직0.3270.0590.1640.3730.2590.2420.0870.2171.0000.1510.3300.0000.2740.0230.1840.1870.933
공안직0.6050.4170.5910.3980.6020.4020.1870.9550.1511.0000.575-0.1610.4900.1800.7760.0000.711
연구직0.4950.2420.2650.3570.3170.2200.3840.5840.3300.5751.0000.1240.452-0.0250.5750.0000.751
지도직0.312-0.166-0.1240.3320.2910.4290.267-0.1810.000-0.1610.1241.0000.1780.386-0.0450.3780.000
계약직0.7210.6340.7880.7490.5900.5650.1690.5140.2740.4900.4520.1781.0000.3750.7650.0000.660
공중보건의0.2070.4530.5240.1530.7240.843-0.4930.1410.0230.180-0.0250.3860.3751.0000.2410.3910.000
기타0.6420.5680.7340.5740.5680.4740.1580.7810.1840.7760.575-0.0450.7650.2411.0000.1320.933
남녀구분0.0000.0000.0190.0000.6870.7750.5160.0000.1870.0000.0000.3780.0000.3910.1321.0000.000
군무원0.7930.8130.9670.7720.6440.5680.6580.9500.9330.7110.7510.0000.6600.0000.9330.0001.000

Missing values

2023-12-12T20:04:51.840148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:04:52.192885image/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

구분남녀구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
0서울12647161517369552697566899205149317413626010630933651110398
1서울114949730246707456973539704763112971890174916311306401
2부산399962511575491483302587430741213036923315103624
3부산3835802318681145627614812170068803262029501611
4대구354073491198361152895489921905683024746372502846
5대구28798191177496724013259111074502524330101096
6인천309712361160787113035525295172304025121482760
7인천33435015140501348280152125409350393361870925
8광주22139224819730951429345721303127016913162182233
9광주194690128488566135845011205600231211320762
구분남녀구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
24경북36017016164415628494367083321640198364309517694
25경북298020414847911557121851669201983241440518
26경남37560237149966696474984678904000184259347405929
27경남38521071632110954291890744022202172392400800
28전북304253311181450362964564249032009713493883582500
29전북2611006114257563091141029014705892512380950
30전남35255136149137110382266073141960245321354624991
31전남268620113365945387111031439401503022310267
32제주11265218416025451041197021015401546917657898
33제주9008053555446102421715013109636920313