Overview

Dataset statistics

Number of variables17
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory156.5 B

Variable types

Text1
Categorical4
Numeric12

Dataset

Description공무원 직종별(정무직, 별정직, 일반직, 경찰, 소방, 교육 등) 연령별 기여금 납부 면제자 현황에 대한 데이터입니다. 50세 미만부터 시작되며 남녀로 구분됩니다.
URLhttps://www.data.go.kr/data/15054021/fileData.do

Alerts

기능직 is highly overall correlated with and 8 other fieldsHigh correlation
별정직 is highly overall correlated with and 10 other fieldsHigh correlation
정무직 is highly overall correlated with and 10 other fieldsHigh correlation
is highly overall correlated with 일반직 and 13 other fieldsHigh correlation
일반직 is highly overall correlated with and 10 other fieldsHigh correlation
경찰 is highly overall correlated with and 13 other fieldsHigh correlation
소방 is highly overall correlated with and 14 other fieldsHigh correlation
교육직 is highly overall correlated with and 9 other fieldsHigh correlation
법관검사 is highly overall correlated with and 13 other fieldsHigh correlation
공안직 is highly overall correlated with and 14 other fieldsHigh correlation
군무원 is highly overall correlated with and 12 other fieldsHigh correlation
연구직 is highly overall correlated with and 13 other fieldsHigh correlation
지도직 is highly overall correlated with and 12 other fieldsHigh correlation
계약직 is highly overall correlated with and 13 other fieldsHigh correlation
기타 is highly overall correlated with and 10 other fieldsHigh correlation
남녀구분 is highly overall correlated with 경찰 and 2 other fieldsHigh correlation
정무직 is highly imbalanced (51.0%)Imbalance
기능직 is highly imbalanced (75.0%)Imbalance
has 1 (4.2%) zerosZeros
일반직 has 1 (4.2%) zerosZeros
경찰 has 6 (25.0%) zerosZeros
소방 has 5 (20.8%) zerosZeros
교육직 has 8 (33.3%) zerosZeros
법관검사 has 13 (54.2%) zerosZeros
공안직 has 4 (16.7%) zerosZeros
군무원 has 4 (16.7%) zerosZeros
연구직 has 10 (41.7%) zerosZeros
지도직 has 12 (50.0%) zerosZeros
계약직 has 8 (33.3%) zerosZeros
기타 has 5 (20.8%) zerosZeros

Reproduction

Analysis started2023-12-12 04:16:13.989444
Analysis finished2023-12-12 04:16:31.437022
Duration17.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T13:16:31.551282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3333333
Min length3

Characters and Unicode

Total characters80
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

Unique0 ?
Unique (%)0.0%

Sample

1st row50세미만
2nd row50세미만
3rd row50세
4th row50세
5th row51세
ValueCountFrequency (%)
50세미만 2
8.3%
50세 2
8.3%
51세 2
8.3%
52세 2
8.3%
53세 2
8.3%
54세 2
8.3%
55세 2
8.3%
56세 2
8.3%
57세 2
8.3%
58세 2
8.3%
Other values (2) 4
16.7%
2023-12-12T13:16:31.886574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 24
30.0%
24
30.0%
0 6
 
7.5%
6 4
 
5.0%
2
 
2.5%
2
 
2.5%
1 2
 
2.5%
2 2
 
2.5%
3 2
 
2.5%
4 2
 
2.5%
Other values (5) 10
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
60.0%
Other Letter 32
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 24
50.0%
0 6
 
12.5%
6 4
 
8.3%
1 2
 
4.2%
2 2
 
4.2%
3 2
 
4.2%
4 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
9 2
 
4.2%
Other Letter
ValueCountFrequency (%)
24
75.0%
2
 
6.2%
2
 
6.2%
2
 
6.2%
2
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 48
60.0%
Hangul 32
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 24
50.0%
0 6
 
12.5%
6 4
 
8.3%
1 2
 
4.2%
2 2
 
4.2%
3 2
 
4.2%
4 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
9 2
 
4.2%
Hangul
ValueCountFrequency (%)
24
75.0%
2
 
6.2%
2
 
6.2%
2
 
6.2%
2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
60.0%
Hangul 32
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 24
50.0%
0 6
 
12.5%
6 4
 
8.3%
1 2
 
4.2%
2 2
 
4.2%
3 2
 
4.2%
4 2
 
4.2%
7 2
 
4.2%
8 2
 
4.2%
9 2
 
4.2%
Hangul
ValueCountFrequency (%)
24
75.0%
2
 
6.2%
2
 
6.2%
2
 
6.2%
2
 
6.2%

남녀구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
12 
12 

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 (%)
12
50.0%
12
50.0%

Length

2023-12-12T13:16:32.027915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:16:32.134013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12
50.0%
12
50.0%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4014.2917
Minimum0
Maximum10567
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:32.255318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1539.5
median4043
Q36188.5
95-th percentile10156.75
Maximum10567
Range10567
Interquartile range (IQR)5649

Descriptive statistics

Standard deviation3658.3526
Coefficient of variation (CV)0.91133203
Kurtosis-1.1837972
Mean4014.2917
Median Absolute Deviation (MAD)3373
Skewness0.41621193
Sum96343
Variance13383544
MonotonicityNot monotonic
2023-12-12T13:16:32.387317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 2
 
8.3%
0 1
 
4.2%
6166 1
 
4.2%
8985 1
 
4.2%
5058 1
 
4.2%
9634 1
 
4.2%
5700 1
 
4.2%
10567 1
 
4.2%
5945 1
 
4.2%
10249 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0 1
4.2%
2 2
8.3%
12 1
4.2%
67 1
4.2%
181 1
4.2%
659 1
4.2%
681 1
4.2%
1132 1
4.2%
1668 1
4.2%
2190 1
4.2%
ValueCountFrequency (%)
10567 1
4.2%
10249 1
4.2%
9634 1
4.2%
8985 1
4.2%
8003 1
4.2%
6256 1
4.2%
6166 1
4.2%
5945 1
4.2%
5700 1
4.2%
5100 1
4.2%

정무직
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
19 
1
5
 
1
4
 
1
17
 
1

Length

Max length2
Median length1
Mean length1.0416667
Min length1

Unique

Unique3 ?
Unique (%)12.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 19
79.2%
1 2
 
8.3%
5 1
 
4.2%
4 1
 
4.2%
17 1
 
4.2%

Length

2023-12-12T13:16:32.508599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:16:32.615693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
79.2%
1 2
 
8.3%
5 1
 
4.2%
4 1
 
4.2%
17 1
 
4.2%

별정직
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
15 
1
3
4
 
1
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 15
62.5%
1 5
 
20.8%
3 2
 
8.3%
4 1
 
4.2%
8 1
 
4.2%

Length

2023-12-12T13:16:32.727019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:16:32.843596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
62.5%
1 5
 
20.8%
3 2
 
8.3%
4 1
 
4.2%
8 1
 
4.2%

일반직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1388.875
Minimum0
Maximum4561
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:32.991684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1225
median1243.5
Q31645.25
95-th percentile4175.25
Maximum4561
Range4561
Interquartile range (IQR)1420.25

Descriptive statistics

Standard deviation1387.6605
Coefficient of variation (CV)0.99912558
Kurtosis0.43292148
Mean1388.875
Median Absolute Deviation (MAD)855
Skewness1.1246068
Sum33333
Variance1925601.8
MonotonicityNot monotonic
2023-12-12T13:16:33.168073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 2
 
8.3%
0 1
 
4.2%
528 1
 
4.2%
1817 1
 
4.2%
1376 1
 
4.2%
4120 1
 
4.2%
1443 1
 
4.2%
4561 1
 
4.2%
1499 1
 
4.2%
4185 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0 1
4.2%
2 2
8.3%
12 1
4.2%
29 1
4.2%
153 1
4.2%
249 1
4.2%
528 1
4.2%
604 1
4.2%
699 1
4.2%
958 1
4.2%
ValueCountFrequency (%)
4561 1
4.2%
4185 1
4.2%
4120 1
4.2%
3247 1
4.2%
2298 1
4.2%
1817 1
4.2%
1588 1
4.2%
1499 1
4.2%
1476 1
4.2%
1443 1
4.2%

경찰
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean718.91667
Minimum0
Maximum2851
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:33.317302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.5
median40
Q31404.25
95-th percentile2693.65
Maximum2851
Range2851
Interquartile range (IQR)1396.75

Descriptive statistics

Standard deviation1067.1843
Coefficient of variation (CV)1.4844339
Kurtosis-0.50128816
Mean718.91667
Median Absolute Deviation (MAD)40
Skewness1.1255635
Sum17254
Variance1138882.3
MonotonicityNot monotonic
2023-12-12T13:16:33.485129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6
25.0%
32 1
 
4.2%
11 1
 
4.2%
699 1
 
4.2%
35 1
 
4.2%
1906 1
 
4.2%
54 1
 
4.2%
2218 1
 
4.2%
41 1
 
4.2%
2704 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
0 6
25.0%
10 1
 
4.2%
11 1
 
4.2%
25 1
 
4.2%
32 1
 
4.2%
35 1
 
4.2%
39 1
 
4.2%
41 1
 
4.2%
46 1
 
4.2%
54 1
 
4.2%
ValueCountFrequency (%)
2851 1
4.2%
2704 1
4.2%
2635 1
4.2%
2345 1
4.2%
2218 1
4.2%
1906 1
4.2%
1237 1
4.2%
699 1
4.2%
366 1
4.2%
54 1
4.2%

소방
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.79167
Minimum0
Maximum538
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:33.665146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8.5
Q3202.75
95-th percentile505.9
Maximum538
Range538
Interquartile range (IQR)200.75

Descriptive statistics

Standard deviation183.19863
Coefficient of variation (CV)1.6099477
Kurtosis0.61308616
Mean113.79167
Median Absolute Deviation (MAD)8.5
Skewness1.4388333
Sum2731
Variance33561.737
MonotonicityNot monotonic
2023-12-12T13:16:33.825232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 5
20.8%
2 4
16.7%
4 1
 
4.2%
200 1
 
4.2%
8 1
 
4.2%
417 1
 
4.2%
10 1
 
4.2%
520 1
 
4.2%
11 1
 
4.2%
538 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
0 5
20.8%
2 4
16.7%
4 1
 
4.2%
6 1
 
4.2%
8 1
 
4.2%
9 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
14 1
 
4.2%
71 1
 
4.2%
ValueCountFrequency (%)
538 1
4.2%
520 1
4.2%
426 1
4.2%
417 1
4.2%
278 1
4.2%
211 1
4.2%
200 1
4.2%
71 1
4.2%
14 1
4.2%
11 1
4.2%

교육직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494.75
Minimum0
Maximum5683
Zeros8
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:33.960460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median400
Q32424
95-th percentile5323.55
Maximum5683
Range5683
Interquartile range (IQR)2424

Descriptive statistics

Standard deviation1880.8441
Coefficient of variation (CV)1.2583001
Kurtosis-0.10039108
Mean1494.75
Median Absolute Deviation (MAD)400
Skewness1.0472664
Sum35874
Variance3537574.4
MonotonicityNot monotonic
2023-12-12T13:16:34.112672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8
33.3%
1 1
 
4.2%
5528 1
 
4.2%
5683 1
 
4.2%
3425 1
 
4.2%
2183 1
 
4.2%
3978 1
 
4.2%
2191 1
 
4.2%
4165 1
 
4.2%
1784 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
0 8
33.3%
1 1
 
4.2%
46 1
 
4.2%
60 1
 
4.2%
338 1
 
4.2%
462 1
 
4.2%
1163 1
 
4.2%
1744 1
 
4.2%
1784 1
 
4.2%
2183 1
 
4.2%
ValueCountFrequency (%)
5683 1
4.2%
5528 1
4.2%
4165 1
4.2%
3978 1
4.2%
3425 1
4.2%
3123 1
4.2%
2191 1
4.2%
2183 1
4.2%
1784 1
4.2%
1744 1
4.2%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2083333
Minimum0
Maximum62
Zeros13
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:34.272075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile19.4
Maximum62
Range62
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation13.266431
Coefficient of variation (CV)2.5471547
Kurtosis15.66227
Mean5.2083333
Median Absolute Deviation (MAD)0
Skewness3.7714989
Sum125
Variance175.99819
MonotonicityNot monotonic
2023-12-12T13:16:34.454006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 13
54.2%
1 5
 
20.8%
8 1
 
4.2%
11 1
 
4.2%
3 1
 
4.2%
20 1
 
4.2%
16 1
 
4.2%
62 1
 
4.2%
ValueCountFrequency (%)
0 13
54.2%
1 5
 
20.8%
3 1
 
4.2%
8 1
 
4.2%
11 1
 
4.2%
16 1
 
4.2%
20 1
 
4.2%
62 1
 
4.2%
ValueCountFrequency (%)
62 1
 
4.2%
20 1
 
4.2%
16 1
 
4.2%
11 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
1 5
 
20.8%
0 13
54.2%

기능직
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
23 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 23
95.8%
1 1
 
4.2%

Length

2023-12-12T13:16:34.639126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:16:34.767905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
95.8%
1 1
 
4.2%

공안직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.791667
Minimum0
Maximum293
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:34.888679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q3104.25
95-th percentile274.2
Maximum293
Range293
Interquartile range (IQR)101.25

Descriptive statistics

Standard deviation101.186
Coefficient of variation (CV)1.4709049
Kurtosis0.25817915
Mean68.791667
Median Absolute Deviation (MAD)14.5
Skewness1.3620826
Sum1651
Variance10238.607
MonotonicityNot monotonic
2023-12-12T13:16:35.063033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 4
16.7%
8 2
 
8.3%
16 2
 
8.3%
3 2
 
8.3%
14 1
 
4.2%
98 1
 
4.2%
10 1
 
4.2%
247 1
 
4.2%
279 1
 
4.2%
21 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
0 4
16.7%
1 1
 
4.2%
3 2
8.3%
8 2
8.3%
10 1
 
4.2%
13 1
 
4.2%
14 1
 
4.2%
16 2
8.3%
18 1
 
4.2%
21 1
 
4.2%
ValueCountFrequency (%)
293 1
4.2%
279 1
4.2%
247 1
4.2%
241 1
4.2%
194 1
4.2%
123 1
4.2%
98 1
4.2%
45 1
4.2%
21 1
4.2%
18 1
4.2%

군무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.375
Minimum0
Maximum270
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:35.244620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median53.5
Q385.75
95-th percentile256.05
Maximum270
Range270
Interquartile range (IQR)73.75

Descriptive statistics

Standard deviation82.221235
Coefficient of variation (CV)1.090829
Kurtosis0.92004371
Mean75.375
Median Absolute Deviation (MAD)40
Skewness1.3514674
Sum1809
Variance6760.3315
MonotonicityNot monotonic
2023-12-12T13:16:35.416344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
16.7%
38 2
 
8.3%
56 1
 
4.2%
24 1
 
4.2%
83 1
 
4.2%
47 1
 
4.2%
228 1
 
4.2%
74 1
 
4.2%
270 1
 
4.2%
70 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 4
16.7%
1 1
 
4.2%
6 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
38 2
8.3%
47 1
 
4.2%
51 1
 
4.2%
56 1
 
4.2%
64 1
 
4.2%
ValueCountFrequency (%)
270 1
4.2%
261 1
4.2%
228 1
4.2%
184 1
4.2%
134 1
4.2%
94 1
4.2%
83 1
4.2%
74 1
4.2%
72 1
4.2%
70 1
4.2%

연구직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.625
Minimum0
Maximum84
Zeros10
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:35.570766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313.5
95-th percentile72.3
Maximum84
Range84
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation24.845282
Coefficient of variation (CV)1.6988227
Kurtosis2.6567398
Mean14.625
Median Absolute Deviation (MAD)2
Skewness1.9183336
Sum351
Variance617.28804
MonotonicityNot monotonic
2023-12-12T13:16:35.750454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 10
41.7%
1 2
 
8.3%
6 2
 
8.3%
3 1
 
4.2%
13 1
 
4.2%
27 1
 
4.2%
11 1
 
4.2%
57 1
 
4.2%
15 1
 
4.2%
75 1
 
4.2%
Other values (3) 3
 
12.5%
ValueCountFrequency (%)
0 10
41.7%
1 2
 
8.3%
3 1
 
4.2%
6 2
 
8.3%
7 1
 
4.2%
11 1
 
4.2%
13 1
 
4.2%
15 1
 
4.2%
27 1
 
4.2%
45 1
 
4.2%
ValueCountFrequency (%)
84 1
4.2%
75 1
4.2%
57 1
4.2%
45 1
4.2%
27 1
4.2%
15 1
4.2%
13 1
4.2%
11 1
4.2%
7 1
4.2%
6 2
8.3%

지도직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0833333
Minimum0
Maximum42
Zeros12
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:35.909460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314
95-th percentile37.55
Maximum42
Range42
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.519444
Coefficient of variation (CV)1.4883791
Kurtosis0.96773483
Mean9.0833333
Median Absolute Deviation (MAD)1
Skewness1.4807472
Sum218
Variance182.77536
MonotonicityNot monotonic
2023-12-12T13:16:36.058532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 12
50.0%
9 2
 
8.3%
2 2
 
8.3%
14 2
 
8.3%
15 1
 
4.2%
35 1
 
4.2%
30 1
 
4.2%
8 1
 
4.2%
38 1
 
4.2%
42 1
 
4.2%
ValueCountFrequency (%)
0 12
50.0%
2 2
 
8.3%
8 1
 
4.2%
9 2
 
8.3%
14 2
 
8.3%
15 1
 
4.2%
30 1
 
4.2%
35 1
 
4.2%
38 1
 
4.2%
42 1
 
4.2%
ValueCountFrequency (%)
42 1
 
4.2%
38 1
 
4.2%
35 1
 
4.2%
30 1
 
4.2%
15 1
 
4.2%
14 2
 
8.3%
9 2
 
8.3%
8 1
 
4.2%
2 2
 
8.3%
0 12
50.0%

계약직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4583333
Minimum0
Maximum54
Zeros8
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:36.193988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile17.7
Maximum54
Range54
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.806333
Coefficient of variation (CV)1.8280773
Kurtosis11.577407
Mean6.4583333
Median Absolute Deviation (MAD)1
Skewness3.1140556
Sum155
Variance139.38949
MonotonicityNot monotonic
2023-12-12T13:16:36.345511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 8
33.3%
1 5
20.8%
2 3
 
12.5%
15 2
 
8.3%
8 1
 
4.2%
18 1
 
4.2%
16 1
 
4.2%
6 1
 
4.2%
54 1
 
4.2%
12 1
 
4.2%
ValueCountFrequency (%)
0 8
33.3%
1 5
20.8%
2 3
 
12.5%
6 1
 
4.2%
8 1
 
4.2%
12 1
 
4.2%
15 2
 
8.3%
16 1
 
4.2%
18 1
 
4.2%
54 1
 
4.2%
ValueCountFrequency (%)
54 1
 
4.2%
18 1
 
4.2%
16 1
 
4.2%
15 2
 
8.3%
12 1
 
4.2%
8 1
 
4.2%
6 1
 
4.2%
2 3
 
12.5%
1 5
20.8%
0 8
33.3%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.25
Minimum0
Maximum375
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T13:16:36.489041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.25
median72.5
Q3170.25
95-th percentile367.95
Maximum375
Range375
Interquartile range (IQR)152

Descriptive statistics

Standard deviation125.24628
Coefficient of variation (CV)1.0773873
Kurtosis-0.0059874043
Mean116.25
Median Absolute Deviation (MAD)66.5
Skewness1.0961053
Sum2790
Variance15686.63
MonotonicityNot monotonic
2023-12-12T13:16:36.647266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5
20.8%
80 1
 
4.2%
44 1
 
4.2%
204 1
 
4.2%
133 1
 
4.2%
369 1
 
4.2%
103 1
 
4.2%
375 1
 
4.2%
109 1
 
4.2%
362 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 5
20.8%
13 1
 
4.2%
20 1
 
4.2%
29 1
 
4.2%
44 1
 
4.2%
45 1
 
4.2%
59 1
 
4.2%
65 1
 
4.2%
80 1
 
4.2%
97 1
 
4.2%
ValueCountFrequency (%)
375 1
4.2%
369 1
4.2%
362 1
4.2%
307 1
4.2%
217 1
4.2%
204 1
4.2%
159 1
4.2%
133 1
4.2%
109 1
4.2%
103 1
4.2%

Interactions

2023-12-12T13:16:29.250914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:14.881143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.480225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.605402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.825566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.964466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.995232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.308342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.953407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.410034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.647945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.948619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.380704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:14.988123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.568713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.739234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.941571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.050191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.105573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.404843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.081683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.517145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.780948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.069524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.510839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:15.110774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.663844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.834955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.044806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.140231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.212652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.821719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.194524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.617752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.890026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.176872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.629176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:15.233927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.743508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.933388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.151432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.236408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.313032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.919924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.323608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.700739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.984149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.273296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.759583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:15.344923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.835693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.046387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.236793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.329605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.423101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.019045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.456374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.787998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.073050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.362202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.904250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:15.755594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.928619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.159541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.320224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.407001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.528295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.115192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.589952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.885594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.194488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.478348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:30.037628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:15.858832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.044237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.249825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.416463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.491222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.634028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.231745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.731017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.012269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.308247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.585404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:30.159866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:15.977710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.140927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.356121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.513788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.585690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.754969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.360819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:24.888501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.133711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.424045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.700252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:30.278135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.103585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.224129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.446028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.611855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.683743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:21.891586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.477060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.009255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.229592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.554308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.795121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:30.662251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.206972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.317032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.542288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.691381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.760355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.009793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.569579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.119140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.311781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.653281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:28.901289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:30.751377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.311994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.392989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.622728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.773343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.831753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.120678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.700687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.202845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.415707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.743928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.004187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:30.849504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:16.393183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:17.491902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:18.721075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:19.867069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:20.905167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:22.218913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.827019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:25.288110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:26.527784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:27.839315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:29.107992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:16:36.775167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남녀구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직기타
구분1.0000.0000.5610.2280.0000.4440.0000.0000.7290.2280.2470.0000.6370.3810.3560.2470.563
남녀구분0.0001.0000.3820.2790.1640.3900.8400.8030.0610.2790.0000.5370.7700.1270.1520.0000.770
0.5610.3821.0000.7900.8090.9030.8200.8780.7970.7901.0000.8510.7980.7900.7220.9280.892
정무직0.2280.2790.7901.0000.9810.5460.9200.7310.5910.9931.0000.8170.8521.0000.8250.9380.652
별정직0.0000.1640.8090.9811.0000.6150.8210.6670.6000.9701.0000.7750.7690.9220.8780.9220.712
일반직0.4440.3900.9030.5460.6151.0000.6820.8160.8730.5460.0000.9070.9060.8790.9180.8110.894
경찰0.0000.8400.8200.9200.8210.6821.0000.9070.4710.9531.0000.9100.9400.9140.7420.8420.854
소방0.0000.8030.8780.7310.6670.8160.9071.0000.2620.7310.7781.0000.9510.8020.6900.7430.802
교육직0.7290.0610.7970.5910.6000.8730.4710.2621.0000.5910.5140.4540.6530.9000.9330.7910.784
법관검사0.2280.2790.7900.9930.9700.5460.9530.7310.5911.0001.0000.8170.8521.0000.7840.9380.652
기능직0.2470.0001.0001.0001.0000.0001.0000.7780.5141.0001.0001.0000.6741.0000.2521.0000.674
공안직0.0000.5370.8510.8170.7750.9070.9101.0000.4540.8171.0001.0000.9000.9410.8760.8270.837
군무원0.6370.7700.7980.8520.7690.9060.9400.9510.6530.8520.6740.9001.0000.8830.8000.8550.949
연구직0.3810.1270.7901.0000.9220.8790.9140.8020.9001.0001.0000.9410.8831.0000.9620.8610.750
지도직0.3560.1520.7220.8250.8780.9180.7420.6900.9330.7840.2520.8760.8000.9621.0000.7990.866
계약직0.2470.0000.9280.9380.9220.8110.8420.7430.7910.9381.0000.8270.8550.8610.7991.0000.833
기타0.5630.7700.8920.6520.7120.8940.8540.8020.7840.6520.6740.8370.9490.7500.8660.8331.000
2023-12-12T13:16:36.962049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남녀구분기능직별정직정무직
남녀구분1.0000.0000.1650.305
기능직0.0001.0000.9290.929
별정직0.1650.9291.0000.800
정무직0.3050.9290.8001.000
2023-12-12T13:16:37.070853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반직경찰소방교육직법관검사공안직군무원연구직지도직계약직기타남녀구분정무직별정직기능직
1.0000.9410.8010.8790.8170.8830.9070.9120.9410.8660.9130.9430.2940.5340.5590.826
일반직0.9411.0000.8510.8990.7040.8030.9550.9770.9000.9150.8370.9780.3540.3530.4190.000
경찰0.8010.8511.0000.9480.4960.6000.8920.8280.7670.7840.7600.8630.5560.7940.6270.853
소방0.8790.8990.9481.0000.5600.7310.9120.8610.8350.8120.8690.9150.5440.5780.5060.522
교육직0.8170.7040.4960.5601.0000.8100.6130.6510.8090.6560.7370.7060.0000.3950.4040.477
법관검사0.8830.8030.6000.7310.8101.0000.6980.7320.9410.8430.8470.8050.3050.8790.7510.929
공안직0.9070.9550.8920.9120.6130.6981.0000.9610.8430.8340.8100.9560.5000.6640.6040.879
군무원0.9120.9770.8280.8610.6510.7320.9611.0000.8290.8710.8230.9530.4980.6750.5550.426
연구직0.9410.9000.7670.8350.8090.9410.8430.8291.0000.9190.8390.9150.0560.9460.8360.879
지도직0.8660.9150.7840.8120.6560.8430.8340.8710.9191.0000.7570.9210.0970.6750.7600.213
계약직0.9130.8370.7600.8690.7370.8470.8100.8230.8390.7571.0000.8520.0000.6490.6080.929
기타0.9430.9780.8630.9150.7060.8050.9560.9530.9150.9210.8521.0000.4980.4220.4870.426
남녀구분0.2940.3540.5560.5440.0000.3050.5000.4980.0560.0970.0000.4981.0000.3050.1650.000
정무직0.5340.3530.7940.5780.3950.8790.6640.6750.9460.6750.6490.4220.3051.0000.8000.929
별정직0.5590.4190.6270.5060.4040.7510.6040.5550.8360.7600.6080.4870.1650.8001.0000.929
기능직0.8260.0000.8530.5220.4770.9290.8790.4260.8790.2130.9290.4260.0000.9290.9291.000

Missing values

2023-12-12T13:16:31.053766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:16:31.332528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분남녀구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직기타
050세미만000000000000000
150세미만200200000000000
250세200200000000000
350세12001200000000000
451세670029322000310000
551세181001530000011400013
652세65900249366140003600120
752세681006040200083800029
853세219000699123771100453810197
953세1132019581064600135100245
구분남녀구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직기타
1456세80031132472345426116380241184273518307
1556세48190014764693123101456112180
1657세1024954418527045381784110293261573015362
1757세5945011499411141653021701582109
1858세1056713456122185202191200279270753816375
1958세570000144354103978101674696103
2059세963443412019064172183160247228844215369
2159세505801137635834251010477141133
2260세이상8985178181769920056836219883451454204
2360세이상616600528114552810824601244