Overview

Dataset statistics

Number of variables17
Number of observations41
Missing cells60
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory155.2 B

Variable types

Text1
Numeric15
Categorical1

Dataset

Description공무원 직종별, 재직년수별 퇴직자 현황 데이터(정무직,별정직,일반직,경찰,소방, 계약직 등)로 1년 미만부터 연 단위로 구분되어 있습니다.
URLhttps://www.data.go.kr/data/15053020/fileData.do

Alerts

is highly overall correlated with 일반직 and 9 other fieldsHigh correlation
정무직 is highly overall correlated with 소방 and 1 other fieldsHigh correlation
별정직 is highly overall correlated with 계약직 and 1 other fieldsHigh correlation
일반직 is highly overall correlated with and 9 other fieldsHigh correlation
경찰 is highly overall correlated with and 7 other fieldsHigh correlation
소방 is highly overall correlated with and 9 other fieldsHigh correlation
교육직 is highly overall correlated with and 7 other fieldsHigh correlation
법관검사 is highly overall correlated with 기타 and 1 other fieldsHigh correlation
공안직 is highly overall correlated with and 9 other fieldsHigh correlation
군무원 is highly overall correlated with and 8 other fieldsHigh correlation
연구직 is highly overall correlated with and 6 other fieldsHigh correlation
지도직 is highly overall correlated with and 6 other fieldsHigh correlation
계약직 is highly overall correlated with 별정직 and 1 other fieldsHigh correlation
기타 is highly overall correlated with and 7 other fieldsHigh correlation
기능직 is highly overall correlated with and 2 other fieldsHigh correlation
정무직 has 17 (41.5%) missing valuesMissing
별정직 has 3 (7.3%) missing valuesMissing
법관검사 has 3 (7.3%) missing valuesMissing
공안직 has 1 (2.4%) missing valuesMissing
연구직 has 1 (2.4%) missing valuesMissing
지도직 has 3 (7.3%) missing valuesMissing
공중보건의 has 32 (78.0%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:53:24.911806
Analysis finished2023-12-12 11:53:55.674026
Duration30.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T20:53:55.846074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.902439
Min length2

Characters and Unicode

Total characters119
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row1년미만
2nd row1년이상
3rd row2년
4th row3년
5th row4년
ValueCountFrequency (%)
1년미만 1
 
2.4%
21년 1
 
2.4%
23년 1
 
2.4%
24년 1
 
2.4%
25년 1
 
2.4%
26년 1
 
2.4%
27년 1
 
2.4%
28년 1
 
2.4%
29년 1
 
2.4%
30년 1
 
2.4%
Other values (31) 31
75.6%
2023-12-12T20:53:56.218848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
34.5%
1 15
 
12.6%
2 14
 
11.8%
3 14
 
11.8%
4 5
 
4.2%
5 4
 
3.4%
6 4
 
3.4%
7 4
 
3.4%
8 4
 
3.4%
9 4
 
3.4%
Other values (5) 10
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
60.5%
Other Letter 47
39.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
20.8%
2 14
19.4%
3 14
19.4%
4 5
 
6.9%
5 4
 
5.6%
6 4
 
5.6%
7 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
0 4
 
5.6%
Other Letter
ValueCountFrequency (%)
41
87.2%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 72
60.5%
Hangul 47
39.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
20.8%
2 14
19.4%
3 14
19.4%
4 5
 
6.9%
5 4
 
5.6%
6 4
 
5.6%
7 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
0 4
 
5.6%
Hangul
ValueCountFrequency (%)
41
87.2%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
60.5%
Hangul 47
39.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
41
87.2%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
ASCII
ValueCountFrequency (%)
1 15
20.8%
2 14
19.4%
3 14
19.4%
4 5
 
6.9%
5 4
 
5.6%
6 4
 
5.6%
7 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
0 4
 
5.6%


Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.2927
Minimum205
Maximum3706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:56.367885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum205
5-th percentile283
Q1333
median641
Q32305
95-th percentile3584
Maximum3706
Range3501
Interquartile range (IQR)1972

Descriptive statistics

Standard deviation1228.2005
Coefficient of variation (CV)0.91568415
Kurtosis-0.95880659
Mean1341.2927
Median Absolute Deviation (MAD)347
Skewness0.81821946
Sum54993
Variance1508476.4
MonotonicityNot monotonic
2023-12-12T20:53:56.506256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
641 2
 
4.9%
3123 1
 
2.4%
2894 1
 
2.4%
307 1
 
2.4%
381 1
 
2.4%
492 1
 
2.4%
575 1
 
2.4%
720 1
 
2.4%
863 1
 
2.4%
1498 1
 
2.4%
Other values (30) 30
73.2%
ValueCountFrequency (%)
205 1
2.4%
255 1
2.4%
283 1
2.4%
294 1
2.4%
307 1
2.4%
308 1
2.4%
310 1
2.4%
314 1
2.4%
322 1
2.4%
332 1
2.4%
ValueCountFrequency (%)
3706 1
2.4%
3611 1
2.4%
3584 1
2.4%
3538 1
2.4%
3459 1
2.4%
3123 1
2.4%
3120 1
2.4%
2894 1
2.4%
2644 1
2.4%
2473 1
2.4%

정무직
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)37.5%
Missing17
Missing (%)41.5%
Infinite0
Infinite (%)0.0%
Mean5.2916667
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:56.640656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37.25
95-th percentile14
Maximum29
Range28
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.50404
Coefficient of variation (CV)1.2291099
Kurtosis7.0359893
Mean5.2916667
Median Absolute Deviation (MAD)2
Skewness2.3967443
Sum127
Variance42.302536
MonotonicityNot monotonic
2023-12-12T20:53:56.787276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 11
26.8%
3 2
 
4.9%
4 2
 
4.9%
8 2
 
4.9%
7 2
 
4.9%
14 2
 
4.9%
29 1
 
2.4%
5 1
 
2.4%
10 1
 
2.4%
(Missing) 17
41.5%
ValueCountFrequency (%)
1 11
26.8%
3 2
 
4.9%
4 2
 
4.9%
5 1
 
2.4%
7 2
 
4.9%
8 2
 
4.9%
10 1
 
2.4%
14 2
 
4.9%
29 1
 
2.4%
ValueCountFrequency (%)
29 1
 
2.4%
14 2
 
4.9%
10 1
 
2.4%
8 2
 
4.9%
7 2
 
4.9%
5 1
 
2.4%
4 2
 
4.9%
3 2
 
4.9%
1 11
26.8%

별정직
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)55.3%
Missing3
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean24.842105
Minimum1
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:56.914238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q318.5
95-th percentile152.65
Maximum188
Range187
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation47.799992
Coefficient of variation (CV)1.9241522
Kurtosis6.1495936
Mean24.842105
Median Absolute Deviation (MAD)4
Skewness2.6424487
Sum944
Variance2284.8393
MonotonicityNot monotonic
2023-12-12T20:53:57.089744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 6
14.6%
1 5
12.2%
2 4
 
9.8%
4 3
 
7.3%
5 2
 
4.9%
12 2
 
4.9%
20 2
 
4.9%
31 1
 
2.4%
14 1
 
2.4%
26 1
 
2.4%
Other values (11) 11
26.8%
(Missing) 3
 
7.3%
ValueCountFrequency (%)
1 5
12.2%
2 4
9.8%
3 6
14.6%
4 3
7.3%
5 2
 
4.9%
6 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
11 1
 
2.4%
ValueCountFrequency (%)
188 1
2.4%
179 1
2.4%
148 1
2.4%
102 1
2.4%
54 1
2.4%
44 1
2.4%
31 1
2.4%
26 1
2.4%
20 2
4.9%
14 1
2.4%

일반직
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.60976
Minimum54
Maximum1503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:57.265036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile94
Q1122
median248
Q3779
95-th percentile1469
Maximum1503
Range1449
Interquartile range (IQR)657

Descriptive statistics

Standard deviation459.16979
Coefficient of variation (CV)0.94946345
Kurtosis-0.037260196
Mean483.60976
Median Absolute Deviation (MAD)146
Skewness1.104672
Sum19828
Variance210836.89
MonotonicityNot monotonic
2023-12-12T20:53:57.458258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
110 2
 
4.9%
856 1
 
2.4%
1012 1
 
2.4%
179 1
 
2.4%
248 1
 
2.4%
306 1
 
2.4%
348 1
 
2.4%
351 1
 
2.4%
468 1
 
2.4%
831 1
 
2.4%
Other values (30) 30
73.2%
ValueCountFrequency (%)
54 1
2.4%
91 1
2.4%
94 1
2.4%
102 1
2.4%
103 1
2.4%
105 1
2.4%
110 2
4.9%
114 1
2.4%
115 1
2.4%
122 1
2.4%
ValueCountFrequency (%)
1503 1
2.4%
1502 1
2.4%
1469 1
2.4%
1442 1
2.4%
1208 1
2.4%
1146 1
2.4%
1012 1
2.4%
917 1
2.4%
856 1
2.4%
831 1
2.4%

경찰
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.02439
Minimum4
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:58.146998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q112
median25
Q361
95-th percentile453
Maximum515
Range511
Interquartile range (IQR)49

Descriptive statistics

Standard deviation145.94819
Coefficient of variation (CV)1.6033965
Kurtosis2.476628
Mean91.02439
Median Absolute Deviation (MAD)14
Skewness1.9444691
Sum3732
Variance21300.874
MonotonicityNot monotonic
2023-12-12T20:53:58.326700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
12 4
 
9.8%
10 4
 
9.8%
7 3
 
7.3%
28 2
 
4.9%
25 2
 
4.9%
24 1
 
2.4%
147 1
 
2.4%
218 1
 
2.4%
339 1
 
2.4%
405 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
4 1
 
2.4%
7 3
7.3%
10 4
9.8%
12 4
9.8%
13 1
 
2.4%
16 1
 
2.4%
18 1
 
2.4%
19 1
 
2.4%
20 1
 
2.4%
21 1
 
2.4%
ValueCountFrequency (%)
515 1
2.4%
479 1
2.4%
453 1
2.4%
405 1
2.4%
339 1
2.4%
314 1
2.4%
218 1
2.4%
148 1
2.4%
147 1
2.4%
72 1
2.4%

소방
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.268293
Minimum2
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:58.465819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q17
median11
Q327
95-th percentile122
Maximum131
Range129
Interquartile range (IQR)20

Descriptive statistics

Standard deviation38.315157
Coefficient of variation (CV)1.3091012
Kurtosis1.4433055
Mean29.268293
Median Absolute Deviation (MAD)7
Skewness1.6612779
Sum1200
Variance1468.0512
MonotonicityNot monotonic
2023-12-12T20:53:58.604352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7 4
 
9.8%
3 3
 
7.3%
8 3
 
7.3%
4 3
 
7.3%
5 2
 
4.9%
13 2
 
4.9%
11 2
 
4.9%
9 2
 
4.9%
131 1
 
2.4%
122 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
2 1
 
2.4%
3 3
7.3%
4 3
7.3%
5 2
4.9%
6 1
 
2.4%
7 4
9.8%
8 3
7.3%
9 2
4.9%
11 2
4.9%
12 1
 
2.4%
ValueCountFrequency (%)
131 1
2.4%
128 1
2.4%
122 1
2.4%
99 1
2.4%
94 1
2.4%
93 1
2.4%
77 1
2.4%
64 1
2.4%
52 1
2.4%
35 1
2.4%

교육직
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.58537
Minimum27
Maximum1905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:58.770952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile34
Q149
median97
Q3267
95-th percentile1106
Maximum1905
Range1878
Interquartile range (IQR)218

Descriptive statistics

Standard deviation436.16396
Coefficient of variation (CV)1.4134305
Kurtosis3.4024168
Mean308.58537
Median Absolute Deviation (MAD)58
Skewness1.9093922
Sum12652
Variance190239
MonotonicityNot monotonic
2023-12-12T20:53:58.976869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
47 3
 
7.3%
72 2
 
4.9%
49 2
 
4.9%
88 2
 
4.9%
163 2
 
4.9%
1021 1
 
2.4%
762 1
 
2.4%
1905 1
 
2.4%
757 1
 
2.4%
725 1
 
2.4%
Other values (25) 25
61.0%
ValueCountFrequency (%)
27 1
 
2.4%
33 1
 
2.4%
34 1
 
2.4%
36 1
 
2.4%
37 1
 
2.4%
39 1
 
2.4%
46 1
 
2.4%
47 3
7.3%
49 2
4.9%
54 1
 
2.4%
ValueCountFrequency (%)
1905 1
2.4%
1107 1
2.4%
1106 1
2.4%
1021 1
2.4%
1017 1
2.4%
1010 1
2.4%
762 1
2.4%
757 1
2.4%
725 1
2.4%
587 1
2.4%

법관검사
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)42.1%
Missing3
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean5.8947368
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:59.139248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37.75
95-th percentile16.15
Maximum20
Range19
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation5.1137836
Coefficient of variation (CV)0.86751685
Kurtosis0.72002028
Mean5.8947368
Median Absolute Deviation (MAD)2
Skewness1.2700817
Sum224
Variance26.150782
MonotonicityNot monotonic
2023-12-12T20:53:59.330639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 7
17.1%
2 6
14.6%
1 6
14.6%
6 3
7.3%
3 3
7.3%
7 2
 
4.9%
8 2
 
4.9%
14 1
 
2.4%
15 1
 
2.4%
16 1
 
2.4%
Other values (6) 6
14.6%
(Missing) 3
7.3%
ValueCountFrequency (%)
1 6
14.6%
2 6
14.6%
3 3
7.3%
4 7
17.1%
5 1
 
2.4%
6 3
7.3%
7 2
 
4.9%
8 2
 
4.9%
9 1
 
2.4%
12 1
 
2.4%
ValueCountFrequency (%)
20 1
2.4%
17 1
2.4%
16 1
2.4%
15 1
2.4%
14 1
2.4%
13 1
2.4%
12 1
2.4%
9 1
2.4%
8 2
4.9%
7 2
4.9%

기능직
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
<NA>
29 
1
2
4
 
1
132
 
1

Length

Max length4
Median length4
Mean length3.1707317
Min length1

Unique

Unique2 ?
Unique (%)4.9%

Sample

1st row4
2nd row1
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 29
70.7%
1 7
 
17.1%
2 3
 
7.3%
4 1
 
2.4%
132 1
 
2.4%

Length

2023-12-12T20:53:59.528795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:53:59.701263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
70.7%
1 7
 
17.1%
2 3
 
7.3%
4 1
 
2.4%
132 1
 
2.4%

공안직
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)62.5%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean34.125
Minimum3
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:53:59.874557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q19
median16
Q328.5
95-th percentile150.75
Maximum197
Range194
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation48.595762
Coefficient of variation (CV)1.4240516
Kurtosis4.9507366
Mean34.125
Median Absolute Deviation (MAD)9
Skewness2.4138787
Sum1365
Variance2361.5481
MonotonicityNot monotonic
2023-12-12T20:54:00.075267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
9 4
 
9.8%
19 4
 
9.8%
7 3
 
7.3%
16 2
 
4.9%
15 2
 
4.9%
6 2
 
4.9%
5 2
 
4.9%
27 2
 
4.9%
11 2
 
4.9%
21 2
 
4.9%
Other values (15) 15
36.6%
ValueCountFrequency (%)
3 1
 
2.4%
5 2
4.9%
6 2
4.9%
7 3
7.3%
8 1
 
2.4%
9 4
9.8%
11 2
4.9%
13 1
 
2.4%
14 1
 
2.4%
15 2
4.9%
ValueCountFrequency (%)
197 1
2.4%
184 1
2.4%
149 1
2.4%
139 1
2.4%
97 1
2.4%
56 1
2.4%
42 1
2.4%
39 1
2.4%
31 1
2.4%
30 1
2.4%

군무원
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.829268
Minimum3
Maximum242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:54:00.287585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q112
median24
Q357
95-th percentile93
Maximum242
Range239
Interquartile range (IQR)45

Descriptive statistics

Standard deviation44.100398
Coefficient of variation (CV)1.0801173
Kurtosis10.2238
Mean40.829268
Median Absolute Deviation (MAD)17
Skewness2.7272031
Sum1674
Variance1944.8451
MonotonicityNot monotonic
2023-12-12T20:54:00.487210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
6 3
 
7.3%
19 2
 
4.9%
74 2
 
4.9%
18 2
 
4.9%
46 1
 
2.4%
23 1
 
2.4%
25 1
 
2.4%
30 1
 
2.4%
24 1
 
2.4%
40 1
 
2.4%
Other values (26) 26
63.4%
ValueCountFrequency (%)
3 1
 
2.4%
4 1
 
2.4%
6 3
7.3%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
10 1
 
2.4%
11 1
 
2.4%
12 1
 
2.4%
13 1
 
2.4%
ValueCountFrequency (%)
242 1
2.4%
135 1
2.4%
93 1
2.4%
90 1
2.4%
84 1
2.4%
74 2
4.9%
66 1
2.4%
60 1
2.4%
59 1
2.4%
57 1
2.4%

연구직
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)50.0%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean9.85
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:54:00.643456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6.5
Q313
95-th percentile29.35
Maximum39
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.365923
Coefficient of variation (CV)0.95085512
Kurtosis2.4743426
Mean9.85
Median Absolute Deviation (MAD)3.5
Skewness1.681872
Sum394
Variance87.720513
MonotonicityNot monotonic
2023-12-12T20:54:00.840666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5 6
14.6%
1 4
 
9.8%
7 3
 
7.3%
3 3
 
7.3%
13 3
 
7.3%
4 3
 
7.3%
2 2
 
4.9%
6 2
 
4.9%
8 2
 
4.9%
16 2
 
4.9%
Other values (10) 10
24.4%
ValueCountFrequency (%)
1 4
9.8%
2 2
 
4.9%
3 3
7.3%
4 3
7.3%
5 6
14.6%
6 2
 
4.9%
7 3
7.3%
8 2
 
4.9%
9 1
 
2.4%
10 1
 
2.4%
ValueCountFrequency (%)
39 1
 
2.4%
36 1
 
2.4%
29 1
 
2.4%
25 1
 
2.4%
23 1
 
2.4%
21 1
 
2.4%
16 2
4.9%
13 3
7.3%
12 1
 
2.4%
11 1
 
2.4%

지도직
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)36.8%
Missing3
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean5.6315789
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:54:01.018204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile18.15
Maximum40
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.4595162
Coefficient of variation (CV)1.324587
Kurtosis11.892443
Mean5.6315789
Median Absolute Deviation (MAD)2
Skewness3.1181133
Sum214
Variance55.644381
MonotonicityNot monotonic
2023-12-12T20:54:01.182544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 11
26.8%
3 6
14.6%
2 4
 
9.8%
4 3
 
7.3%
5 3
 
7.3%
6 3
 
7.3%
8 1
 
2.4%
7 1
 
2.4%
19 1
 
2.4%
18 1
 
2.4%
Other values (4) 4
 
9.8%
(Missing) 3
 
7.3%
ValueCountFrequency (%)
1 11
26.8%
2 4
 
9.8%
3 6
14.6%
4 3
 
7.3%
5 3
 
7.3%
6 3
 
7.3%
7 1
 
2.4%
8 1
 
2.4%
11 1
 
2.4%
12 1
 
2.4%
ValueCountFrequency (%)
40 1
 
2.4%
19 1
 
2.4%
18 1
 
2.4%
17 1
 
2.4%
12 1
 
2.4%
11 1
 
2.4%
8 1
 
2.4%
7 1
 
2.4%
6 3
7.3%
5 3
7.3%

계약직
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.390244
Minimum2
Maximum596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:54:01.369579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q19
median22
Q365
95-th percentile427
Maximum596
Range594
Interquartile range (IQR)56

Descriptive statistics

Standard deviation138.8544
Coefficient of variation (CV)1.6853257
Kurtosis5.4456244
Mean82.390244
Median Absolute Deviation (MAD)16
Skewness2.4433765
Sum3378
Variance19280.544
MonotonicityNot monotonic
2023-12-12T20:54:01.560872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 4
 
9.8%
13 2
 
4.9%
7 2
 
4.9%
5 2
 
4.9%
53 2
 
4.9%
297 1
 
2.4%
16 1
 
2.4%
12 1
 
2.4%
11 1
 
2.4%
17 1
 
2.4%
Other values (24) 24
58.5%
ValueCountFrequency (%)
2 1
 
2.4%
3 1
 
2.4%
5 2
4.9%
7 2
4.9%
8 1
 
2.4%
9 4
9.8%
11 1
 
2.4%
12 1
 
2.4%
13 2
4.9%
16 1
 
2.4%
ValueCountFrequency (%)
596 1
2.4%
462 1
2.4%
427 1
2.4%
332 1
2.4%
297 1
2.4%
173 1
2.4%
128 1
2.4%
121 1
2.4%
93 1
2.4%
77 1
2.4%

공중보건의
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)88.9%
Missing32
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean138.55556
Minimum1
Maximum1021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:54:01.766004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q15
median25
Q372
95-th percentile645.4
Maximum1021
Range1020
Interquartile range (IQR)67

Descriptive statistics

Standard deviation332.26161
Coefficient of variation (CV)2.3980389
Kurtosis8.7915625
Mean138.55556
Median Absolute Deviation (MAD)20
Skewness2.9541711
Sum1247
Variance110397.78
MonotonicityNot monotonic
2023-12-12T20:54:01.920860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 2
 
4.9%
1 1
 
2.4%
6 1
 
2.4%
1021 1
 
2.4%
82 1
 
2.4%
25 1
 
2.4%
30 1
 
2.4%
72 1
 
2.4%
(Missing) 32
78.0%
ValueCountFrequency (%)
1 1
2.4%
5 2
4.9%
6 1
2.4%
25 1
2.4%
30 1
2.4%
72 1
2.4%
82 1
2.4%
1021 1
2.4%
ValueCountFrequency (%)
1021 1
2.4%
82 1
2.4%
72 1
2.4%
30 1
2.4%
25 1
2.4%
6 1
2.4%
5 2
4.9%
1 1
2.4%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.82927
Minimum16
Maximum1413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T20:54:02.073878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q147
median74
Q3147
95-th percentile1046
Maximum1413
Range1397
Interquartile range (IQR)100

Descriptive statistics

Standard deviation334.12041
Coefficient of variation (CV)1.7417593
Kurtosis7.4422918
Mean191.82927
Median Absolute Deviation (MAD)43
Skewness2.8718937
Sum7865
Variance111636.45
MonotonicityNot monotonic
2023-12-12T20:54:02.213859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
151 2
 
4.9%
136 2
 
4.9%
92 2
 
4.9%
1413 1
 
2.4%
16 1
 
2.4%
27 1
 
2.4%
35 1
 
2.4%
57 1
 
2.4%
75 1
 
2.4%
95 1
 
2.4%
Other values (28) 28
68.3%
ValueCountFrequency (%)
16 1
2.4%
20 1
2.4%
24 1
2.4%
25 1
2.4%
27 1
2.4%
30 1
2.4%
31 1
2.4%
33 1
2.4%
35 1
2.4%
38 1
2.4%
ValueCountFrequency (%)
1413 1
2.4%
1328 1
2.4%
1046 1
2.4%
812 1
2.4%
343 1
2.4%
262 1
2.4%
165 1
2.4%
164 1
2.4%
151 2
4.9%
147 1
2.4%

Interactions

2023-12-12T20:53:53.120879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:25.763498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:28.021258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.078035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.898760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.569905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:35.145892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.302230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.242805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.343997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:43.507479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.242049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.360495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.265617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.324624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:53.244641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:25.864769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:28.152078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.203387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.994241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.676839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:35.251531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.406883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.374366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.502076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:43.609291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.381952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.472105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.410513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.433686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:53.366756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.020403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:28.265328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.322410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.109218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.769245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:35.732056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.514136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.483636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.656774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:43.720423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.511325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.614896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.512553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.560306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:53.496253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.149792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:28.735328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.451433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.212263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.890572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:35.890651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.631906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.602290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.835096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:43.832247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.659887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.764554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.617050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.710498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:53.638338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.265607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:28.845818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.552662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.328668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.017220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.012657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.738204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.725998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.966294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:43.984743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.797412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.906733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.733211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.842109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:53.766917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.402717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:28.949777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.654662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.434696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.130742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.135655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.840674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.845999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.087399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.075111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.936405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.027693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.856403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.957134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:53.898962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.543667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.059955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.770031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.565901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.234923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.260076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.957211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.973271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.204785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.190783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.076176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.146342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.965268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.072155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.032855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.706635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.161459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:30.887078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.682316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.334316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.360941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.099812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:40.127990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.314843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.303107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.223212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.271450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:50.096433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.191061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.163824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:26.890351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.283415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.016070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.802673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.429699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.481474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.245156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:40.265728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.427032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.414692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.362108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.406563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:50.228981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.312073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.286413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:27.015428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.422925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.137669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:32.919035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.536391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.598363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.386324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:40.408437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.527744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.542186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.499494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.546625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:50.336597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.416559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.397988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:27.146149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.509188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.268652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.027790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.624191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.699046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.512911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:40.578314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.616788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.648427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.646583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.671374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:50.427251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.521303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.526118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:27.324643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.623113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.399867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.145983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.720403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.819277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.639814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:40.738940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.738662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.755952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.790447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.790474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:50.538548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.635911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.638354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:27.568988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.760636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.533901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.267930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.826112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:36.936413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.796327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:40.891050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.851844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.879382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:46.942378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:48.916752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:50.661032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.766733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.772547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:27.748567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.862807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.660171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.370500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:34.926808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.061357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:38.928154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.042092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:42.967145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:44.992235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.085942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.019511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.098856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.885648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:54.881562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:27.884604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:29.953040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:31.773564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:33.460682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:35.030261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:37.189633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:39.079335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:41.192906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:43.057644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:45.107157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:47.215327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:49.134557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:51.203624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:53:52.988997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:54:02.379101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.6780.7760.9320.8030.8150.8740.0000.9010.8600.8220.8060.7630.5931.0000.797
정무직1.0000.6781.0000.2110.6970.6110.1960.4080.0000.0000.7470.7840.5820.0000.0001.0000.786
별정직1.0000.7760.2111.0000.7160.0000.0000.0000.0000.8120.3940.9010.5170.0000.9000.0000.964
일반직1.0000.9320.6970.7161.0000.7630.7420.8590.0000.8620.8910.7850.7720.5610.4790.0000.789
경찰1.0000.8030.6110.0000.7631.0000.9180.8820.0000.0000.8710.5590.9280.7580.000NaN0.000
소방1.0000.8150.1960.0000.7420.9181.0000.8480.0000.0000.8060.5590.8090.6930.0000.0000.000
교육직1.0000.8740.4080.0000.8590.8820.8481.0000.0000.0000.8410.6190.6890.7770.000NaN0.000
법관검사1.0000.0000.0000.0000.0000.0000.0000.0001.0000.8200.0000.0000.0000.0000.0000.0000.000
기능직1.0000.9010.0000.8120.8620.0000.0000.0000.8201.0000.0000.4740.0000.0000.4740.0000.203
공안직1.0000.8600.7470.3940.8910.8710.8060.8410.0000.0001.0000.8090.8380.5150.0000.0000.777
군무원1.0000.8220.7840.9010.7850.5590.5590.6190.0000.4740.8091.0000.4920.4070.7101.0000.865
연구직1.0000.8060.5820.5170.7720.9280.8090.6890.0000.0000.8380.4921.0000.4150.0000.0000.381
지도직1.0000.7630.0000.0000.5610.7580.6930.7770.0000.0000.5150.4070.4151.0000.0000.3960.000
계약직1.0000.5930.0000.9000.4790.0000.0000.0000.0000.4740.0000.7100.0000.0001.0001.0000.851
공중보건의1.0001.0001.0000.0000.000NaN0.000NaN0.0000.0000.0001.0000.0000.3961.0001.0001.000
기타1.0000.7970.7860.9640.7890.0000.0000.0000.0000.2030.7770.8650.3810.0000.8511.0001.000
2023-12-12T20:54:02.649008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직일반직경찰소방교육직법관검사공안직군무원연구직지도직계약직공중보건의기타기능직
1.0000.4710.0810.9490.8460.8150.772-0.4740.8430.8080.6590.710-0.072-0.1510.7660.603
정무직0.4711.0000.1050.4400.1500.5100.3050.2160.5870.3990.4700.0360.097-0.0700.4730.000
별정직0.0810.1051.000-0.139-0.279-0.209-0.437-0.456-0.051-0.0510.102-0.3940.908-0.0840.5490.426
일반직0.9490.440-0.1391.0000.8990.8520.862-0.3540.8680.7950.6880.794-0.278-0.4770.6360.613
경찰0.8460.150-0.2790.8991.0000.8480.814-0.3080.7420.7570.6290.751-0.400-0.4890.4990.000
소방0.8150.510-0.2090.8520.8481.0000.767-0.2240.8040.7850.7440.657-0.253-0.3770.5730.000
교육직0.7720.305-0.4370.8620.8140.7671.000-0.1000.7290.7120.4870.883-0.544-0.1170.3180.000
법관검사-0.4740.216-0.456-0.354-0.308-0.224-0.1001.000-0.226-0.312-0.248-0.159-0.319-0.205-0.7070.726
공안직0.8430.587-0.0510.8680.7420.8040.729-0.2261.0000.7240.7930.668-0.150-0.1600.6230.000
군무원0.8080.399-0.0510.7950.7570.7850.712-0.3120.7241.0000.5270.648-0.136-0.4270.5650.329
연구직0.6590.4700.1020.6880.6290.7440.487-0.2480.7930.5271.0000.4410.0870.3930.6240.000
지도직0.7100.036-0.3940.7940.7510.6570.883-0.1590.6680.6480.4411.000-0.4730.4200.2760.000
계약직-0.0720.0970.908-0.278-0.400-0.253-0.544-0.319-0.150-0.1360.087-0.4731.000-0.1420.4040.329
공중보건의-0.151-0.070-0.084-0.477-0.489-0.377-0.117-0.205-0.160-0.4270.3930.420-0.1421.000-0.3680.000
기타0.7660.4730.5490.6360.4990.5730.318-0.7070.6230.5650.6240.2760.404-0.3681.0000.079
기능직0.6030.0000.4260.6130.0000.0000.0000.7260.0000.3290.0000.0000.3290.0000.0791.000

Missing values

2023-12-12T20:53:55.060206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:53:55.382811image/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.
2023-12-12T20:53:55.560877image/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

구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
01년미만31233148856242772<NA>4272427229711413
11년이상3120291797123919159413013513446261328
22년2249410255728161631<NA>31901034275812
33년35848188441232112821286016559610211046
44년12451543982512751<NA>273713417382343
55년10201442372813463<NA>14211313325262
66년6411202062511492<NA>1178112825147
77년550131162187331<NA>1918<NA>19330136
88년478<NA>1414719634319106<NA>657292
99년32252010310336211163163<NA>58
구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
3131년22021431012147945871<NA>97462169<NA>165
3232년289410311462181281010<NA><NA>1394325417<NA>151
3333년3611143150333913111064<NA>1978436822<NA>164
3434년345989144240512210175<NA>1846623720<NA>151
3535년35381614694539911072114957391919<NA>117
3636년264432120831493725<NA><NA>5674291818<NA>104
3737년247311917479777571<NA>399312175<NA>74
3838년230511779515647623<NA>21745122<NA>66
3939년1956116351481810211<NA>7411118<NA>63
4040년이상3706<NA><NA>150272819052<NA><NA>542403<NA>118