Overview

Dataset statistics

Number of variables12
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory105.6 B

Variable types

Categorical6
Text2
Numeric4

Dataset

Description아산시시설관리공단 채용통계현황 데이터 입니다. 채용연도, 회차, 공고일자, 채용직급, 채용구분, 채용분야, 채용인원, 지원인원, 필기대상인원, 면접대상인원, 최종합격인원, 경쟁률 정보를 제공합니다.
Author아산시시설관리공단
URLhttps://www.data.go.kr/data/15125858/fileData.do

Alerts

연도 has constant value ""Constant
공고일자 is highly overall correlated with 회차 and 1 other fieldsHigh correlation
회차 is highly overall correlated with 공고일자 and 1 other fieldsHigh correlation
채용인원 is highly overall correlated with 최종합격인원High correlation
지원인원 is highly overall correlated with 면접대상인원 and 1 other fieldsHigh correlation
면접대상인원 is highly overall correlated with 지원인원 and 1 other fieldsHigh correlation
최종합격인원 is highly overall correlated with 채용인원 and 2 other fieldsHigh correlation
직급 is highly overall correlated with 필기(인적성)대상인원High correlation
채용구분 is highly overall correlated with 회차 and 1 other fieldsHigh correlation
필기(인적성)대상인원 is highly overall correlated with 직급High correlation
필기(인적성)대상인원 is highly imbalanced (58.9%)Imbalance
최종합격인원 has 2 (6.9%) zerosZeros

Reproduction

Analysis started2024-01-06 12:49:04.174067
Analysis finished2024-01-06 12:49:11.731608
Duration7.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
2022
29 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 29
100.0%

Length

2024-01-06T12:49:11.979863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T12:49:12.270780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 29
100.0%

회차
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
제8회
7 
제2회
4 
제3회
4 
제7회
4 
제3회 재공고
3 
Other values (4)
7 

Length

Max length7
Median length3
Mean length3.4137931
Min length3

Unique

Unique2 ?
Unique (%)6.9%

Sample

1st row제1회
2nd row제2회
3rd row제2회
4th row제2회
5th row제2회

Common Values

ValueCountFrequency (%)
제8회 7
24.1%
제2회 4
13.8%
제3회 4
13.8%
제7회 4
13.8%
제3회 재공고 3
10.3%
제5회 3
10.3%
제6회 2
 
6.9%
제1회 1
 
3.4%
제4회 1
 
3.4%

Length

2024-01-06T12:49:12.621866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T12:49:13.117647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제8회 7
21.9%
제3회 7
21.9%
제2회 4
12.5%
제7회 4
12.5%
재공고 3
9.4%
제5회 3
9.4%
제6회 2
 
6.2%
제1회 1
 
3.1%
제4회 1
 
3.1%

공고일자
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2022-11-10
11 
2022-01-06
5 
2022-03-02
4 
2022-05-06
4 
2022-03-22
3 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-06
2nd row2022-01-06
3rd row2022-01-06
4th row2022-01-06
5th row2022-01-06

Common Values

ValueCountFrequency (%)
2022-11-10 11
37.9%
2022-01-06 5
17.2%
2022-03-02 4
 
13.8%
2022-05-06 4
 
13.8%
2022-03-22 3
 
10.3%
2022-07-27 2
 
6.9%

Length

2024-01-06T12:49:13.696283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T12:49:14.033935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-10 11
37.9%
2022-01-06 5
17.2%
2022-03-02 4
 
13.8%
2022-05-06 4
 
13.8%
2022-03-22 3
 
10.3%
2022-07-27 2
 
6.9%

직급
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
기간제
9 
실버직
8 
단기계약직
4 
체험형 인턴
3 
운전8급
2 
Other values (3)
3 

Length

Max length6
Median length3
Mean length3.7586207
Min length3

Unique

Unique3 ?
Unique (%)10.3%

Sample

1st row실버직
2nd row기간제
3rd row기간제
4th row기간제
5th row기간제

Common Values

ValueCountFrequency (%)
기간제 9
31.0%
실버직 8
27.6%
단기계약직 4
13.8%
체험형 인턴 3
 
10.3%
운전8급 2
 
6.9%
기계8급 1
 
3.4%
전산8급 1
 
3.4%
환경8급 1
 
3.4%

Length

2024-01-06T12:49:14.539644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T12:49:15.017361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기간제 9
28.1%
실버직 8
25.0%
단기계약직 4
12.5%
체험형 3
 
9.4%
인턴 3
 
9.4%
운전8급 2
 
6.2%
기계8급 1
 
3.1%
전산8급 1
 
3.1%
환경8급 1
 
3.1%

채용구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
신규채용
24 
휴직대체
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신규채용
2nd row휴직대체
3rd row휴직대체
4th row휴직대체
5th row휴직대체

Common Values

ValueCountFrequency (%)
신규채용 24
82.8%
휴직대체 5
 
17.2%

Length

2024-01-06T12:49:15.517364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T12:49:15.994994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규채용 24
82.8%
휴직대체 5
 
17.2%

채용분야
Text

Distinct19
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-01-06T12:49:16.364053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.1034483
Min length2

Characters and Unicode

Total characters148
Distinct characters66
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)41.4%

Sample

1st row주차관제센터
2nd row학예보조
3rd row녹지(수목관리)
4th row회원관리
5th row행정
ValueCountFrequency (%)
행정 6
15.0%
및 4
 
10.0%
주차관제센터 3
 
7.5%
기술 3
 
7.5%
공영버스 3
 
7.5%
야간경비 2
 
5.0%
레저스포츠운영 2
 
5.0%
환경미화 2
 
5.0%
운전 2
 
5.0%
요금징수 1
 
2.5%
Other values (12) 12
30.0%
2024-01-06T12:49:17.331477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
7.4%
관 6
 
4.1%
행 6
 
4.1%
정 6
 
4.1%
스 5
 
3.4%
영 5
 
3.4%
제 4
 
2.7%
및 4
 
2.7%
기 4
 
2.7%
경 4
 
2.7%
Other values (56) 93
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
89.2%
Space Separator 11
 
7.4%
Uppercase Letter 3
 
2.0%
Open Punctuation 1
 
0.7%
Close Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 6
 
4.5%
행 6
 
4.5%
정 6
 
4.5%
스 5
 
3.8%
영 5
 
3.8%
제 4
 
3.0%
및 4
 
3.0%
기 4
 
3.0%
경 4
 
3.0%
운 4
 
3.0%
Other values (50) 84
63.6%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
X 1
33.3%
K 1
33.3%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
89.2%
Common 13
 
8.8%
Latin 3
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 6
 
4.5%
행 6
 
4.5%
정 6
 
4.5%
스 5
 
3.8%
영 5
 
3.8%
제 4
 
3.0%
및 4
 
3.0%
기 4
 
3.0%
경 4
 
3.0%
운 4
 
3.0%
Other values (50) 84
63.6%
Common
ValueCountFrequency (%)
11
84.6%
( 1
 
7.7%
) 1
 
7.7%
Latin
ValueCountFrequency (%)
T 1
33.3%
X 1
33.3%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
89.2%
ASCII 16
 
10.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
68.8%
T 1
 
6.2%
X 1
 
6.2%
( 1
 
6.2%
K 1
 
6.2%
) 1
 
6.2%
Hangul
ValueCountFrequency (%)
관 6
 
4.5%
행 6
 
4.5%
정 6
 
4.5%
스 5
 
3.8%
영 5
 
3.8%
제 4
 
3.0%
및 4
 
3.0%
기 4
 
3.0%
경 4
 
3.0%
운 4
 
3.0%
Other values (50) 84
63.6%

채용인원
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9310345
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-06T12:49:17.815716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5061501
Coefficient of variation (CV)0.85503944
Kurtosis0.35504538
Mean2.9310345
Median Absolute Deviation (MAD)1
Skewness1.2682005
Sum85
Variance6.2807882
MonotonicityNot monotonic
2024-01-06T12:49:18.547289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 12
41.4%
2 6
20.7%
3 3
 
10.3%
8 2
 
6.9%
6 2
 
6.9%
4 2
 
6.9%
7 1
 
3.4%
9 1
 
3.4%
ValueCountFrequency (%)
1 12
41.4%
2 6
20.7%
3 3
 
10.3%
4 2
 
6.9%
6 2
 
6.9%
7 1
 
3.4%
8 2
 
6.9%
9 1
 
3.4%
ValueCountFrequency (%)
9 1
 
3.4%
8 2
 
6.9%
7 1
 
3.4%
6 2
 
6.9%
4 2
 
6.9%
3 3
 
10.3%
2 6
20.7%
1 12
41.4%

지원인원
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0689655
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-06T12:49:18.921228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q13
median6
Q310
95-th percentile23.4
Maximum29
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.180982
Coefficient of variation (CV)0.88995076
Kurtosis2.483741
Mean8.0689655
Median Absolute Deviation (MAD)4
Skewness1.6151242
Sum234
Variance51.566502
MonotonicityNot monotonic
2024-01-06T12:49:19.484463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 5
17.2%
7 3
10.3%
1 2
 
6.9%
3 2
 
6.9%
10 2
 
6.9%
6 2
 
6.9%
5 2
 
6.9%
8 2
 
6.9%
4 2
 
6.9%
14 2
 
6.9%
Other values (5) 5
17.2%
ValueCountFrequency (%)
1 2
 
6.9%
2 5
17.2%
3 2
 
6.9%
4 2
 
6.9%
5 2
 
6.9%
6 2
 
6.9%
7 3
10.3%
8 2
 
6.9%
10 2
 
6.9%
11 1
 
3.4%
ValueCountFrequency (%)
29 1
 
3.4%
27 1
 
3.4%
18 1
 
3.4%
16 1
 
3.4%
14 2
6.9%
11 1
 
3.4%
10 2
6.9%
8 2
6.9%
7 3
10.3%
6 2
6.9%

필기(인적성)대상인원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
해당없음
24 
21
 
1
12
 
1
11
 
1
26
 
1

Length

Max length4
Median length4
Mean length3.6206897
Min length1

Unique

Unique5 ?
Unique (%)17.2%

Sample

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

Common Values

ValueCountFrequency (%)
해당없음 24
82.8%
21 1
 
3.4%
12 1
 
3.4%
11 1
 
3.4%
26 1
 
3.4%
5 1
 
3.4%

Length

2024-01-06T12:49:20.004051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T12:49:20.345311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당없음 24
82.8%
21 1
 
3.4%
12 1
 
3.4%
11 1
 
3.4%
26 1
 
3.4%
5 1
 
3.4%

면접대상인원
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0689655
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-06T12:49:20.663465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile14.8
Maximum18
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7278131
Coefficient of variation (CV)0.77901466
Kurtosis0.16210892
Mean6.0689655
Median Absolute Deviation (MAD)3
Skewness0.94032763
Sum176
Variance22.352217
MonotonicityNot monotonic
2024-01-06T12:49:21.019792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 5
17.2%
1 4
13.8%
3 4
13.8%
7 3
10.3%
10 2
 
6.9%
6 2
 
6.9%
5 2
 
6.9%
8 2
 
6.9%
12 2
 
6.9%
16 1
 
3.4%
Other values (2) 2
 
6.9%
ValueCountFrequency (%)
1 4
13.8%
2 5
17.2%
3 4
13.8%
5 2
 
6.9%
6 2
 
6.9%
7 3
10.3%
8 2
 
6.9%
10 2
 
6.9%
12 2
 
6.9%
13 1
 
3.4%
ValueCountFrequency (%)
18 1
 
3.4%
16 1
 
3.4%
13 1
 
3.4%
12 2
6.9%
10 2
6.9%
8 2
6.9%
7 3
10.3%
6 2
6.9%
5 2
6.9%
3 4
13.8%

최종합격인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5172414
Minimum0
Maximum9
Zeros2
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-06T12:49:21.381296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11
median2
Q33
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3997126
Coefficient of variation (CV)0.9533105
Kurtosis1.7262441
Mean2.5172414
Median Absolute Deviation (MAD)1
Skewness1.5677669
Sum73
Variance5.7586207
MonotonicityNot monotonic
2024-01-06T12:49:21.751490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 12
41.4%
2 6
20.7%
8 2
 
6.9%
3 2
 
6.9%
5 2
 
6.9%
0 2
 
6.9%
4 2
 
6.9%
9 1
 
3.4%
ValueCountFrequency (%)
0 2
 
6.9%
1 12
41.4%
2 6
20.7%
3 2
 
6.9%
4 2
 
6.9%
5 2
 
6.9%
8 2
 
6.9%
9 1
 
3.4%
ValueCountFrequency (%)
9 1
 
3.4%
8 2
 
6.9%
5 2
 
6.9%
4 2
 
6.9%
3 2
 
6.9%
2 6
20.7%
1 12
41.4%
0 2
 
6.9%

경쟁률
Text

Distinct17
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-01-06T12:49:22.133466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.4482759
Min length4

Characters and Unicode

Total characters129
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)37.9%

Sample

1st row2:01
2nd row0.4:1
3rd row3:01
4th row10:01
5th row10:01
ValueCountFrequency (%)
2:01 8
27.6%
1.2:1 2
 
6.9%
0.4:1 2
 
6.9%
7:01 2
 
6.9%
10:01 2
 
6.9%
1:01 2
 
6.9%
1.5:1 1
 
3.4%
11:01 1
 
3.4%
1.8:1 1
 
3.4%
2.5:1 1
 
3.4%
Other values (7) 7
24.1%
2024-01-06T12:49:22.862348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 39
30.2%
: 29
22.5%
0 23
17.8%
2 11
 
8.5%
. 10
 
7.8%
5 4
 
3.1%
4 3
 
2.3%
7 3
 
2.3%
3 3
 
2.3%
8 3
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
69.8%
Other Punctuation 39
30.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39
43.3%
0 23
25.6%
2 11
 
12.2%
5 4
 
4.4%
4 3
 
3.3%
7 3
 
3.3%
3 3
 
3.3%
8 3
 
3.3%
6 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
: 29
74.4%
. 10
 
25.6%

Most occurring scripts

ValueCountFrequency (%)
Common 129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39
30.2%
: 29
22.5%
0 23
17.8%
2 11
 
8.5%
. 10
 
7.8%
5 4
 
3.1%
4 3
 
2.3%
7 3
 
2.3%
3 3
 
2.3%
8 3
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39
30.2%
: 29
22.5%
0 23
17.8%
2 11
 
8.5%
. 10
 
7.8%
5 4
 
3.1%
4 3
 
2.3%
7 3
 
2.3%
3 3
 
2.3%
8 3
 
2.3%

Interactions

2024-01-06T12:49:09.556676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:05.557228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:06.877835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:08.126581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:09.850456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:05.863169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:07.192851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:08.505532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:10.121645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:06.137519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:07.444038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:08.882893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:10.377259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:06.400241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:07.761143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:49:09.221084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T12:49:23.160899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회차공고일자직급채용구분채용분야채용인원지원인원필기(인적성)대상인원면접대상인원최종합격인원경쟁률
회차1.0001.0000.6220.8770.0000.4190.6990.6760.7270.5050.601
공고일자1.0001.0000.2460.9430.0000.0000.3040.0000.5640.0000.000
직급0.6220.2461.0000.7070.9860.8020.8340.9410.3710.8370.898
채용구분0.8770.9430.7071.0000.8200.0000.2570.0000.5680.3540.737
채용분야0.0000.0000.9860.8201.0000.7590.6980.9610.0000.8560.751
채용인원0.4190.0000.8020.0000.7591.0000.9080.3060.8550.9860.152
지원인원0.6990.3040.8340.2570.6980.9081.0000.6490.9480.9340.683
필기(인적성)대상인원0.6760.0000.9410.0000.9610.3060.6491.0000.0000.3060.966
면접대상인원0.7270.5640.3710.5680.0000.8550.9480.0001.0000.8650.764
최종합격인원0.5050.0000.8370.3540.8560.9860.9340.3060.8651.0000.000
경쟁률0.6010.0000.8980.7370.7510.1520.6830.9660.7640.0001.000
2024-01-06T12:49:23.505290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직급필기(인적성)대상인원공고일자회차채용구분
직급1.0000.8180.0860.3400.470
필기(인적성)대상인원0.8181.0000.0000.3730.000
공고일자0.0860.0001.0000.9330.725
회차0.3400.3730.9331.0000.784
채용구분0.4700.0000.7250.7841.000
2024-01-06T12:49:23.701359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채용인원지원인원면접대상인원최종합격인원회차공고일자직급채용구분필기(인적성)대상인원
채용인원1.0000.3960.4910.8950.1840.0000.3750.0000.133
지원인원0.3961.0000.8660.6030.4150.1320.4120.1430.403
면접대상인원0.4910.8661.0000.6730.4140.2890.1320.3550.000
최종합격인원0.8950.6030.6731.0000.2460.0000.4170.2160.133
회차0.1840.4150.4140.2461.0000.9330.3400.7840.373
공고일자0.0000.1320.2890.0000.9331.0000.0860.7250.000
직급0.3750.4120.1320.4170.3400.0861.0000.4700.818
채용구분0.0000.1430.3550.2160.7840.7250.4701.0000.000
필기(인적성)대상인원0.1330.4030.0000.1330.3730.0000.8180.0001.000

Missing values

2024-01-06T12:49:10.965689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T12:49:11.511025image/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

연도회차공고일자직급채용구분채용분야채용인원지원인원필기(인적성)대상인원면접대상인원최종합격인원경쟁률
02022제1회2022-01-06실버직신규채용주차관제센터816해당없음1682:01
12022제2회2022-01-06기간제휴직대체학예보조31해당없음110.4:1
22022제2회2022-01-06기간제휴직대체녹지(수목관리)13해당없음313:01
32022제2회2022-01-06기간제휴직대체회원관리110해당없음10110:01
42022제2회2022-01-06기간제휴직대체행정110해당없음10110:01
52022제3회2022-03-02실버직신규채용야간경비27해당없음723.5:1
62022제3회2022-03-02단기계약직신규채용레저스포츠운영66해당없음631:01
72022제3회2022-03-02단기계약직신규채용종량제 배송15해당없음515:01
82022제3회2022-03-02체험형 인턴신규채용행정 및 기술78해당없음851.2:1
92022제3회 재공고2022-03-22단기계약직신규채용레저스포츠운영24해당없음322:01
연도회차공고일자직급채용구분채용분야채용인원지원인원필기(인적성)대상인원면접대상인원최종합격인원경쟁률
192022제7회2022-11-10전산8급신규채용일반전산111112111:01
202022제7회2022-11-10운전8급신규채용공영버스 운전427261246.8:1
212022제7회2022-11-10환경8급신규채용설비조작 및 선별255322.5:1
222022제8회2022-11-10실버직신규채용야간경비23해당없음321.5:1
232022제8회2022-11-10실버직신규채용KTX 요금징수918해당없음1892:01
242022제8회2022-11-10실버직신규채용주차관제센터814해당없음1381.8:1
252022제8회2022-11-10실버직신규채용환경미화36해당없음632:01
262022제8회2022-11-10기간제신규채용운전12해당없음102:01
272022제8회2022-11-10기간제신규채용행정18해당없음818:01
282022제8회2022-11-10기간제신규채용체육12해당없음212:01