Overview

Dataset statistics

Number of variables18
Number of observations22
Missing cells2
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory166.0 B

Variable types

Text1
Categorical2
Numeric15

Dataset

Description공무원 직종별(일반직, 정무직, 별정직, 경찰, 소방, 교육직, 기능직, 공안직, 군무원, 연구직 등), 급여종류별(퇴직일시금,퇴직연금공제일시금,정상연금,조기연금,유족급여, 유족연금 등) 퇴직자 수 데이터입니다.
URLhttps://www.data.go.kr/data/15053013/fileData.do

Alerts

is highly overall correlated with 정무직 and 14 other fieldsHigh correlation
정무직 is highly overall correlated with and 14 other fieldsHigh correlation
별정직 is highly overall correlated with and 14 other fieldsHigh correlation
일반직 is highly overall correlated with and 14 other fieldsHigh correlation
경찰 is highly overall correlated with and 13 other fieldsHigh correlation
소방 is highly overall correlated with and 13 other fieldsHigh correlation
교육직 is highly overall correlated with and 12 other fieldsHigh correlation
법관검사 is highly overall correlated with and 14 other fieldsHigh correlation
기능직 is highly overall correlated with and 14 other fieldsHigh correlation
공안직 is highly overall correlated with and 14 other fieldsHigh correlation
군무원 is highly overall correlated with and 14 other fieldsHigh correlation
연구직 is highly overall correlated with and 14 other fieldsHigh correlation
지도직 is highly overall correlated with and 14 other fieldsHigh correlation
계약직 is highly overall correlated with and 14 other fieldsHigh correlation
기타 is highly overall correlated with and 14 other fieldsHigh correlation
공중보건의 is highly overall correlated with and 14 other fieldsHigh correlation
공중보건의 is highly imbalanced (56.1%)Imbalance
소방 has 1 (4.5%) missing valuesMissing
공안직 has 1 (4.5%) missing valuesMissing
has unique valuesUnique
정무직 has 14 (63.6%) zerosZeros
별정직 has 10 (45.5%) zerosZeros
법관검사 has 6 (27.3%) zerosZeros
기능직 has 8 (36.4%) zerosZeros
공안직 has 1 (4.5%) zerosZeros
연구직 has 3 (13.6%) zerosZeros
지도직 has 5 (22.7%) zerosZeros
계약직 has 3 (13.6%) zerosZeros

Reproduction

Analysis started2023-12-12 10:43:50.233666
Analysis finished2023-12-12 10:44:20.128993
Duration29.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T19:44:20.259714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length6.2727273
Min length4

Characters and Unicode

Total characters138
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row퇴직급여계(공제일시금제외)
2nd row퇴직급여계(공제일시금제외)
3rd row퇴직일시금
4th row퇴직일시금
5th row퇴직연금일시금
ValueCountFrequency (%)
퇴직급여계(공제일시금제외 2
9.1%
퇴직일시금 2
9.1%
퇴직연금일시금 2
9.1%
정상연금 2
9.1%
조기연금 2
9.1%
수급대기자 2
9.1%
퇴직연금공제일시금 2
9.1%
유족급여계 2
9.1%
유족일시금 2
9.1%
유족연금일시금 2
9.1%
2023-12-12T19:44:20.664641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
17.4%
12
 
8.7%
12
 
8.7%
12
 
8.7%
8
 
5.8%
8
 
5.8%
8
 
5.8%
8
 
5.8%
6
 
4.3%
6
 
4.3%
Other values (13) 34
24.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
97.1%
Open Punctuation 2
 
1.4%
Close Punctuation 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24
17.9%
12
 
9.0%
12
 
9.0%
12
 
9.0%
8
 
6.0%
8
 
6.0%
8
 
6.0%
8
 
6.0%
6
 
4.5%
6
 
4.5%
Other values (11) 30
22.4%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
97.1%
Common 4
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24
17.9%
12
 
9.0%
12
 
9.0%
12
 
9.0%
8
 
6.0%
8
 
6.0%
8
 
6.0%
8
 
6.0%
6
 
4.5%
6
 
4.5%
Other values (11) 30
22.4%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
97.1%
ASCII 4
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
24
17.9%
12
 
9.0%
12
 
9.0%
12
 
9.0%
8
 
6.0%
8
 
6.0%
8
 
6.0%
8
 
6.0%
6
 
4.5%
6
 
4.5%
Other values (11) 30
22.4%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

남여구분
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
11 
11 

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 (%)
11
50.0%
11
50.0%

Length

2023-12-12T19:44:20.830198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:44:20.955117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11
50.0%
11
50.0%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4619.1818
Minimum47
Maximum32321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:21.084645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile81.55
Q1219.25
median450.5
Q34576
95-th percentile21526.4
Maximum32321
Range32274
Interquartile range (IQR)4356.75

Descriptive statistics

Standard deviation8557.9941
Coefficient of variation (CV)1.8527078
Kurtosis4.882529
Mean4619.1818
Median Absolute Deviation (MAD)364
Skewness2.2854452
Sum101622
Variance73239263
MonotonicityNot monotonic
2023-12-12T19:44:21.249101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
32321 1
 
4.5%
432 1
 
4.5%
81 1
 
4.5%
306 1
 
4.5%
92 1
 
4.5%
126 1
 
4.5%
47 1
 
4.5%
95 1
 
4.5%
220 1
 
4.5%
527 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
47 1
4.5%
81 1
4.5%
92 1
4.5%
95 1
4.5%
126 1
4.5%
219 1
4.5%
220 1
4.5%
232 1
4.5%
306 1
4.5%
347 1
4.5%
ValueCountFrequency (%)
32321 1
4.5%
21743 1
4.5%
17411 1
4.5%
9874 1
4.5%
7185 1
4.5%
5452 1
4.5%
1948 1
4.5%
1397 1
4.5%
1098 1
4.5%
527 1
4.5%

정무직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.818182
Minimum0
Maximum110
Zeros14
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:21.413154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.25
95-th percentile57.85
Maximum110
Range110
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation26.404611
Coefficient of variation (CV)2.4407623
Kurtosis9.9234987
Mean10.818182
Median Absolute Deviation (MAD)0
Skewness3.0850601
Sum238
Variance697.20346
MonotonicityNot monotonic
2023-12-12T19:44:21.557445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
63.6%
2 2
 
9.1%
110 1
 
4.5%
9 1
 
4.5%
36 1
 
4.5%
5 1
 
4.5%
59 1
 
4.5%
15 1
 
4.5%
ValueCountFrequency (%)
0 14
63.6%
2 2
 
9.1%
5 1
 
4.5%
9 1
 
4.5%
15 1
 
4.5%
36 1
 
4.5%
59 1
 
4.5%
110 1
 
4.5%
ValueCountFrequency (%)
110 1
 
4.5%
59 1
 
4.5%
36 1
 
4.5%
15 1
 
4.5%
9 1
 
4.5%
5 1
 
4.5%
2 2
 
9.1%
0 14
63.6%

별정직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.681818
Minimum0
Maximum515
Zeros10
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:21.719524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q326
95-th percentile406.55
Maximum515
Range515
Interquartile range (IQR)26

Descriptive statistics

Standard deviation139.21323
Coefficient of variation (CV)2.2941506
Kurtosis6.5763237
Mean60.681818
Median Absolute Deviation (MAD)1.5
Skewness2.6800605
Sum1335
Variance19380.323
MonotonicityNot monotonic
2023-12-12T19:44:21.884637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
45.5%
2 2
 
9.1%
515 1
 
4.5%
151 1
 
4.5%
420 1
 
4.5%
140 1
 
4.5%
8 1
 
4.5%
53 1
 
4.5%
4 1
 
4.5%
32 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 10
45.5%
1 1
 
4.5%
2 2
 
9.1%
4 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
32 1
 
4.5%
53 1
 
4.5%
140 1
 
4.5%
151 1
 
4.5%
ValueCountFrequency (%)
515 1
4.5%
420 1
4.5%
151 1
4.5%
140 1
4.5%
53 1
4.5%
32 1
4.5%
8 1
4.5%
7 1
4.5%
4 1
4.5%
2 2
9.1%

일반직
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1704.6818
Minimum20
Maximum12987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:22.081697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile32.15
Q188.5
median194
Q31263
95-th percentile9839.45
Maximum12987
Range12967
Interquartile range (IQR)1174.5

Descriptive statistics

Standard deviation3444.8116
Coefficient of variation (CV)2.0207945
Kurtosis6.1463041
Mean1704.6818
Median Absolute Deviation (MAD)160.5
Skewness2.5819872
Sum37503
Variance11866727
MonotonicityNot monotonic
2023-12-12T19:44:22.258761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
35 2
 
9.1%
12987 1
 
4.5%
5326 1
 
4.5%
32 1
 
4.5%
132 1
 
4.5%
50 1
 
4.5%
20 1
 
4.5%
87 1
 
4.5%
217 1
 
4.5%
93 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
20 1
4.5%
32 1
4.5%
35 2
9.1%
50 1
4.5%
87 1
4.5%
93 1
4.5%
95 1
4.5%
132 1
4.5%
176 1
4.5%
178 1
4.5%
ValueCountFrequency (%)
12987 1
4.5%
10077 1
4.5%
5326 1
4.5%
2991 1
4.5%
1469 1
4.5%
1401 1
4.5%
849 1
4.5%
629 1
4.5%
414 1
4.5%
217 1
4.5%

경찰
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.90909
Minimum1
Maximum3424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:22.383952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median20.5
Q382.75
95-th percentile2862.4
Maximum3424
Range3423
Interquartile range (IQR)73.75

Descriptive statistics

Standard deviation936.63038
Coefficient of variation (CV)2.8304764
Kurtosis8.2793758
Mean330.90909
Median Absolute Deviation (MAD)19
Skewness3.0710841
Sum7280
Variance877276.47
MonotonicityNot monotonic
2023-12-12T19:44:22.547229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
9 2
 
9.1%
20 2
 
9.1%
2 2
 
9.1%
3424 1
 
4.5%
3 1
 
4.5%
62 1
 
4.5%
4 1
 
4.5%
14 1
 
4.5%
96 1
 
4.5%
52 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
1 1
4.5%
2 2
9.1%
3 1
4.5%
4 1
4.5%
9 2
9.1%
13 1
4.5%
14 1
4.5%
20 2
9.1%
21 1
4.5%
41 1
4.5%
ValueCountFrequency (%)
3424 1
4.5%
3004 1
4.5%
172 1
4.5%
148 1
4.5%
96 1
4.5%
84 1
4.5%
79 1
4.5%
62 1
4.5%
52 1
4.5%
41 1
4.5%

소방
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)81.0%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean111.14286
Minimum1
Maximum1093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:22.773374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median14
Q332
95-th percentile880
Maximum1093
Range1092
Interquartile range (IQR)28

Descriptive statistics

Standard deviation293.77871
Coefficient of variation (CV)2.6432531
Kurtosis8.103336
Mean111.14286
Median Absolute Deviation (MAD)12
Skewness3.0163126
Sum2334
Variance86305.929
MonotonicityNot monotonic
2023-12-12T19:44:22.923642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 2
 
9.1%
14 2
 
9.1%
1 2
 
9.1%
5 2
 
9.1%
38 1
 
4.5%
15 1
 
4.5%
12 1
 
4.5%
4 1
 
4.5%
32 1
 
4.5%
1093 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
1 2
9.1%
2 2
9.1%
3 1
4.5%
4 1
4.5%
5 2
9.1%
7 1
4.5%
12 1
4.5%
14 2
9.1%
15 1
4.5%
23 1
4.5%
ValueCountFrequency (%)
1093 1
4.5%
880 1
4.5%
82 1
4.5%
70 1
4.5%
38 1
4.5%
32 1
4.5%
31 1
4.5%
23 1
4.5%
15 1
4.5%
14 2
9.1%

교육직
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1139.9091
Minimum11
Maximum7427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:23.048086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile15.75
Q143.25
median102
Q3376.75
95-th percentile6180.95
Maximum7427
Range7416
Interquartile range (IQR)333.5

Descriptive statistics

Standard deviation2273.7175
Coefficient of variation (CV)1.9946481
Kurtosis2.5901356
Mean1139.9091
Median Absolute Deviation (MAD)71.5
Skewness1.9873755
Sum25078
Variance5169791.3
MonotonicityNot monotonic
2023-12-12T19:44:23.180743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
30 2
 
9.1%
4831 1
 
4.5%
85 1
 
4.5%
40 1
 
4.5%
44 1
 
4.5%
43 1
 
4.5%
11 1
 
4.5%
15 1
 
4.5%
94 1
 
4.5%
89 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
11 1
4.5%
15 1
4.5%
30 2
9.1%
40 1
4.5%
43 1
4.5%
44 1
4.5%
85 1
4.5%
89 1
4.5%
94 1
4.5%
99 1
4.5%
ValueCountFrequency (%)
7427 1
4.5%
6252 1
4.5%
4831 1
4.5%
4359 1
4.5%
473 1
4.5%
424 1
4.5%
235 1
4.5%
173 1
4.5%
111 1
4.5%
108 1
4.5%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.409091
Minimum0
Maximum187
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:23.324057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median2
Q316.5
95-th percentile121.85
Maximum187
Range187
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation46.193856
Coefficient of variation (CV)2.3800113
Kurtosis9.3941373
Mean19.409091
Median Absolute Deviation (MAD)2
Skewness3.1130342
Sum427
Variance2133.8723
MonotonicityNot monotonic
2023-12-12T19:44:23.460270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 6
27.3%
2 4
18.2%
1 4
18.2%
21 2
 
9.1%
187 1
 
4.5%
15 1
 
4.5%
24 1
 
4.5%
127 1
 
4.5%
17 1
 
4.5%
3 1
 
4.5%
ValueCountFrequency (%)
0 6
27.3%
1 4
18.2%
2 4
18.2%
3 1
 
4.5%
15 1
 
4.5%
17 1
 
4.5%
21 2
 
9.1%
24 1
 
4.5%
127 1
 
4.5%
187 1
 
4.5%
ValueCountFrequency (%)
187 1
 
4.5%
127 1
 
4.5%
24 1
 
4.5%
21 2
 
9.1%
17 1
 
4.5%
15 1
 
4.5%
3 1
 
4.5%
2 4
18.2%
1 4
18.2%
0 6
27.3%

기능직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.818182
Minimum0
Maximum106
Zeros8
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:23.632706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35.75
95-th percentile83.85
Maximum106
Range106
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation29.069107
Coefficient of variation (CV)2.1036854
Kurtosis5.466838
Mean13.818182
Median Absolute Deviation (MAD)2
Skewness2.475126
Sum304
Variance845.01299
MonotonicityNot monotonic
2023-12-12T19:44:23.841858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 8
36.4%
2 4
18.2%
1 2
 
9.1%
106 1
 
4.5%
43 1
 
4.5%
6 1
 
4.5%
9 1
 
4.5%
3 1
 
4.5%
5 1
 
4.5%
86 1
 
4.5%
ValueCountFrequency (%)
0 8
36.4%
1 2
 
9.1%
2 4
18.2%
3 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
9 1
 
4.5%
36 1
 
4.5%
43 1
 
4.5%
86 1
 
4.5%
ValueCountFrequency (%)
106 1
 
4.5%
86 1
 
4.5%
43 1
 
4.5%
36 1
 
4.5%
9 1
 
4.5%
6 1
 
4.5%
5 1
 
4.5%
3 1
 
4.5%
2 4
18.2%
1 2
9.1%

공안직
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)90.5%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean122
Minimum0
Maximum1119
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:23.982935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median18
Q364
95-th percentile864
Maximum1119
Range1119
Interquartile range (IQR)59

Descriptive statistics

Standard deviation294.02245
Coefficient of variation (CV)2.4100201
Kurtosis8.1812174
Mean122
Median Absolute Deviation (MAD)16
Skewness2.9987306
Sum2562
Variance86449.2
MonotonicityNot monotonic
2023-12-12T19:44:24.111748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 2
 
9.1%
18 2
 
9.1%
1119 1
 
4.5%
0 1
 
4.5%
1 1
 
4.5%
16 1
 
4.5%
3 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
23 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
0 1
4.5%
1 1
4.5%
2 2
9.1%
3 1
4.5%
5 1
4.5%
6 1
4.5%
9 1
4.5%
11 1
4.5%
16 1
4.5%
18 2
9.1%
ValueCountFrequency (%)
1119 1
4.5%
864 1
4.5%
124 1
4.5%
98 1
4.5%
90 1
4.5%
64 1
4.5%
49 1
4.5%
40 1
4.5%
23 1
4.5%
18 2
9.1%

군무원
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.81818
Minimum1
Maximum1169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:24.259860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13.75
median16.5
Q3123.75
95-th percentile632.95
Maximum1169
Range1168
Interquartile range (IQR)120

Descriptive statistics

Standard deviation280.09873
Coefficient of variation (CV)2.0033069
Kurtosis8.8403778
Mean139.81818
Median Absolute Deviation (MAD)14.5
Skewness2.8735922
Sum3076
Variance78455.299
MonotonicityNot monotonic
2023-12-12T19:44:24.425886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 3
 
13.6%
3 2
 
9.1%
12 2
 
9.1%
102 1
 
4.5%
1 1
 
4.5%
7 1
 
4.5%
6 1
 
4.5%
21 1
 
4.5%
30 1
 
4.5%
1169 1
 
4.5%
Other values (8) 8
36.4%
ValueCountFrequency (%)
1 1
 
4.5%
2 3
13.6%
3 2
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
12 2
9.1%
21 1
 
4.5%
30 1
 
4.5%
34 1
 
4.5%
ValueCountFrequency (%)
1169 1
4.5%
648 1
4.5%
347 1
4.5%
335 1
4.5%
163 1
4.5%
131 1
4.5%
102 1
4.5%
38 1
4.5%
34 1
4.5%
30 1
4.5%

연구직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.818182
Minimum0
Maximum272
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:24.943056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q324.75
95-th percentile201.25
Maximum272
Range272
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation70.212356
Coefficient of variation (CV)2.206674
Kurtosis7.752714
Mean31.818182
Median Absolute Deviation (MAD)3
Skewness2.876281
Sum700
Variance4929.7749
MonotonicityNot monotonic
2023-12-12T19:44:25.121544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 4
18.2%
1 4
18.2%
0 3
13.6%
272 1
 
4.5%
73 1
 
4.5%
31 1
 
4.5%
29 1
 
4.5%
4 1
 
4.5%
6 1
 
4.5%
208 1
 
4.5%
Other values (4) 4
18.2%
ValueCountFrequency (%)
0 3
13.6%
1 4
18.2%
2 4
18.2%
4 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
11 1
 
4.5%
24 1
 
4.5%
25 1
 
4.5%
29 1
 
4.5%
ValueCountFrequency (%)
272 1
4.5%
208 1
4.5%
73 1
4.5%
31 1
4.5%
29 1
4.5%
25 1
4.5%
24 1
4.5%
11 1
4.5%
6 1
4.5%
5 1
4.5%

지도직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.136364
Minimum0
Maximum163
Zeros5
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:25.274739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q39.5
95-th percentile130.35
Maximum163
Range163
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation43.36588
Coefficient of variation (CV)2.2661505
Kurtosis7.4611341
Mean19.136364
Median Absolute Deviation (MAD)2
Skewness2.8650627
Sum421
Variance1880.5996
MonotonicityNot monotonic
2023-12-12T19:44:25.422976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5
22.7%
1 4
18.2%
2 3
13.6%
5 2
 
9.1%
163 1
 
4.5%
42 1
 
4.5%
8 1
 
4.5%
10 1
 
4.5%
135 1
 
4.5%
23 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 5
22.7%
1 4
18.2%
2 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
8 1
 
4.5%
10 1
 
4.5%
14 1
 
4.5%
23 1
 
4.5%
42 1
 
4.5%
ValueCountFrequency (%)
163 1
 
4.5%
135 1
 
4.5%
42 1
 
4.5%
23 1
 
4.5%
14 1
 
4.5%
10 1
 
4.5%
8 1
 
4.5%
6 1
 
4.5%
5 2
9.1%
2 3
13.6%

계약직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.59091
Minimum0
Maximum1473
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:25.550700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median7.5
Q3117.25
95-th percentile1058.25
Maximum1473
Range1473
Interquartile range (IQR)115

Descriptive statistics

Standard deviation426.67738
Coefficient of variation (CV)1.9430558
Kurtosis2.9793263
Mean219.59091
Median Absolute Deviation (MAD)7.5
Skewness2.0100572
Sum4831
Variance182053.59
MonotonicityNot monotonic
2023-12-12T19:44:25.710915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3
13.6%
1 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
1473 1
 
4.5%
129 1
 
4.5%
2 1
 
4.5%
8 1
 
4.5%
82 1
 
4.5%
4 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0 3
13.6%
1 2
9.1%
2 1
 
4.5%
3 2
9.1%
4 1
 
4.5%
7 2
9.1%
8 1
 
4.5%
21 1
 
4.5%
24 1
 
4.5%
49 1
 
4.5%
ValueCountFrequency (%)
1473 1
4.5%
1065 1
4.5%
930 1
4.5%
799 1
4.5%
223 1
4.5%
129 1
4.5%
82 1
4.5%
49 1
4.5%
24 1
4.5%
21 1
4.5%

공중보건의
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
20 
1192
 
2

Length

Max length4
Median length1
Mean length1.2727273
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1192
2nd row0
3rd row1192
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
90.9%
1192 2
 
9.1%

Length

2023-12-12T19:44:25.886898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:44:26.004089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
90.9%
1192 2
 
9.1%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597.68182
Minimum1
Maximum3680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:44:26.171981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q18.5
median32
Q3297
95-th percentile2794.45
Maximum3680
Range3679
Interquartile range (IQR)288.5

Descriptive statistics

Standard deviation1094.89
Coefficient of variation (CV)1.8318944
Kurtosis2.302384
Mean597.68182
Median Absolute Deviation (MAD)29
Skewness1.8520713
Sum13149
Variance1198784
MonotonicityNot monotonic
2023-12-12T19:44:26.369740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7 2
 
9.1%
3680 1
 
4.5%
2815 1
 
4.5%
1 1
 
4.5%
17 1
 
4.5%
2 1
 
4.5%
4 1
 
4.5%
14 1
 
4.5%
38 1
 
4.5%
13 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
4 1
4.5%
5 1
4.5%
7 2
9.1%
13 1
4.5%
14 1
4.5%
17 1
4.5%
19 1
4.5%
26 1
4.5%
ValueCountFrequency (%)
3680 1
4.5%
2815 1
4.5%
2404 1
4.5%
2033 1
4.5%
1206 1
4.5%
308 1
4.5%
264 1
4.5%
151 1
4.5%
79 1
4.5%
56 1
4.5%

Interactions

2023-12-12T19:44:17.621608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.033451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.839338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.884140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.675807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.603681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.255278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.300313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:04.217880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.160708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.948732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:09.946471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.715248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.523297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.411707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:17.771405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.159567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.959219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.997074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.811516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.728234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.366738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.408278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:04.396666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.279933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.050387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.044183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.846684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.632519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.528763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:17.890056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.277042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.068067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.123593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.913404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.841270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.466637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.515122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:04.531167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.399157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.183839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.150187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.968499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.754329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.654107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:17.999958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.403577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.187541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.224173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.058091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.945772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.610170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.617682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:04.669466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.518304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.298341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.250406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.099575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.873685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.764474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.116909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.528874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.303863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.349368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.193536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.067699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.728451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.723502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:04.814416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.658850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.404475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.362672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.208461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.995008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.873420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.265288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.639612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.430410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.460014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.335777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.173611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.833620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.836026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:04.935981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.775098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.516817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.480854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.336770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.140881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.011362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.386277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.762790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.548406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.565402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.463490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.270751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.978372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.933802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.050633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.870809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.608367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.607080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.465823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.266526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.116288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.528232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:51.891737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.658293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.683035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.596067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.372434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:01.087446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.056312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.181136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.985728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.706017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.748469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.599332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.408114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.221609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.684407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.003752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.803022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.791001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.736574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.504027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:01.234797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.190284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.317802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.110046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.833228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.881963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.728457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.538274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.345618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.826209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.114072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:53.901617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:55.889816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.840258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.613027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:01.329685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.295200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.431181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.222276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:08.956849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:10.988420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.832752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.656545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.449136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:18.950623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.254080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.319926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.002454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:57.943931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.715763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:01.426044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.398403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.544393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.338700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:09.050105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.096519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:12.939078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.803697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.564522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:19.086833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.357375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.414407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.106481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.063581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.812457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:01.539197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.505288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.677248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.470898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:09.151350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.199107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.054199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:14.945258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.685472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:19.221465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.460848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.523300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.265194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.199891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:59.909532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:01.639922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.642345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.786667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.592348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:09.257088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.317552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.176682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.064091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.803062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:19.353247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.578080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.654340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.407028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.318652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.027607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.088861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.794597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:05.911451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.724602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:09.719361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.464870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.291545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.184773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:16.932326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:19.459916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:52.724597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:54.771734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:56.543158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:58.448594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:00.136121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:02.194034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:03.972491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:06.043661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:07.838585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:09.824636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:11.582809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:13.401255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:15.288054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:17.466332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:44:26.554274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남여구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
구분1.0000.0000.5200.3170.4460.7550.3150.0000.4800.2790.4800.2710.4800.7570.4800.4790.0000.479
남여구분0.0001.0000.0000.1820.1980.0000.0600.0140.0000.0000.0000.0000.0000.0000.0000.0820.0000.082
0.5200.0001.0000.8811.0001.0001.0001.0001.0000.7400.7161.0001.0001.0001.0001.0001.0001.000
정무직0.3170.1820.8811.0000.9870.8181.0001.0000.9280.8620.9280.8070.9650.9640.9280.8891.0000.889
별정직0.4460.1981.0000.9871.0000.9151.0001.0000.9640.6730.8480.8880.9860.9930.9641.0001.0001.000
일반직0.7550.0001.0000.8180.9151.0001.0001.0001.0000.7970.7321.0000.9401.0001.0000.9920.9510.992
경찰0.3150.0601.0001.0001.0001.0001.0001.0001.0000.7280.6591.0001.0001.0001.0001.0000.4161.000
소방0.0000.0141.0001.0001.0001.0001.0001.0001.0000.7250.6551.0001.0001.0001.0001.0000.4141.000
교육직0.4800.0001.0000.9280.9641.0001.0001.0001.0000.7260.9281.0000.9850.9871.0000.8890.4980.889
법관검사0.2790.0000.7400.8620.6730.7970.7280.7250.7261.0000.8620.9440.6750.7270.7260.7960.8370.796
기능직0.4800.0000.7160.9280.8480.7320.6590.6550.9280.8621.0000.8070.8500.9290.9280.7300.4980.730
공안직0.2710.0001.0000.8070.8881.0001.0001.0001.0000.9440.8071.0000.8881.0001.0001.0000.8311.000
군무원0.4800.0001.0000.9650.9860.9401.0001.0000.9850.6750.8500.8881.0000.9860.9850.9370.6820.937
연구직0.7570.0001.0000.9640.9931.0001.0001.0000.9870.7270.9291.0000.9861.0000.9871.0000.6821.000
지도직0.4800.0001.0000.9280.9641.0001.0001.0001.0000.7260.9281.0000.9850.9871.0000.8890.4980.889
계약직0.4790.0821.0000.8891.0000.9921.0001.0000.8890.7960.7301.0000.9371.0000.8891.0001.0001.000
공중보건의0.0000.0001.0001.0001.0000.9510.4160.4140.4980.8370.4980.8310.6820.6820.4981.0001.0001.000
기타0.4790.0821.0000.8891.0000.9921.0001.0000.8890.7960.7301.0000.9371.0000.8891.0001.0001.000
2023-12-12T19:44:26.750317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공중보건의남여구분
공중보건의1.0000.000
남여구분0.0001.000
2023-12-12T19:44:26.876363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직기타남여구분공중보건의
1.0000.8430.8680.9950.7640.7720.8640.7300.8330.9270.9490.9300.9500.8800.9630.0000.866
정무직0.8431.0000.8680.8430.5850.6290.7410.6600.7330.7980.8340.8560.8480.8280.8260.1830.922
별정직0.8680.8681.0000.8620.6560.7440.7310.6820.8060.8240.8720.8260.8280.9160.9070.2040.922
일반직0.9950.8430.8621.0000.7500.7670.8620.7120.8450.9280.9560.9450.9510.8770.9530.0000.715
경찰0.7640.5850.6560.7501.0000.9590.4070.7080.7470.8950.8280.6240.7290.5620.7960.0710.634
소방0.7720.6290.7440.7670.9591.0000.4590.7190.7700.8690.8350.6610.7050.6540.8080.0000.629
교육직0.8640.7410.7310.8620.4070.4591.0000.5740.6320.6980.7220.8520.8200.8230.7660.0000.552
법관검사0.7300.6600.6820.7120.7080.7190.5741.0000.6960.7210.7160.6290.7200.6780.7100.0000.597
기능직0.8330.7330.8060.8450.7470.7700.6320.6961.0000.8620.8410.7700.8520.7350.8260.0000.552
공안직0.9270.7980.8240.9280.8950.8690.6980.7210.8621.0000.9720.8760.9340.7800.9200.0000.588
군무원0.9490.8340.8720.9560.8280.8350.7220.7160.8410.9721.0000.9090.9280.8230.9430.0000.749
연구직0.9300.8560.8260.9450.6240.6610.8520.6290.7700.8760.9091.0000.9110.8760.8650.0000.749
지도직0.9500.8480.8280.9510.7290.7050.8200.7200.8520.9340.9280.9111.0000.8230.9110.0000.552
계약직0.8800.8280.9160.8770.5620.6540.8230.6780.7350.7800.8230.8760.8231.0000.8840.0000.894
기타0.9630.8260.9070.9530.7960.8080.7660.7100.8260.9200.9430.8650.9110.8841.0000.0000.894
남여구분0.0000.1830.2040.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
공중보건의0.8660.9220.9220.7150.6340.6290.5520.5970.5520.5880.7490.7490.5520.8940.8940.0001.000

Missing values

2023-12-12T19:44:19.629416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:44:19.896163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T19:44:20.075644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분남여구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
0퇴직급여계(공제일시금제외)323211105151298734241093483118710611191169272163147311923680
1퇴직급여계(공제일시금제외)1741191515326843174272143124335734293002815
2퇴직일시금71853642014691488223515698347318106511922033
3퇴직일시금54525140140120144242140163291079902404
4퇴직연금일시금10980841417270108219493845490151
5퇴직연금일시금4690021013317320986224019
6정상연금217435953100773004880435924386464820813522301206
7정상연금9874242991417625205641312523210308
8조기연금34702178212330021834517026
9조기연금21900951210501<NA>321405
구분남여구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
12퇴직연금공제일시금43202176521485221812157056
13퇴직연금공제일시금23201932<NA>1111002108013
14유족급여계52700217963289322321222038
15유족급여계22000879494106621307
16유족일시금9500351412150022001014
17유족일시금4700204111002200304
18유족연금일시금126005020530205700007
19유족연금일시금9200352143103311002
20유족연금30600132621544121612221017
21유족연금8100323240001110001