Overview

Dataset statistics

Number of variables10
Number of observations73
Missing cells56
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory84.8 B

Variable types

Categorical5
Text2
Numeric2
DateTime1

Dataset

Description제주특별자치도에서 관리하는 지방공무원 임용시험 관련한 데이터로 구분, 직급, 직렬, 선발인원, 응시현황(명) 등 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15056421/fileData.do

Alerts

해당연도 has constant value ""Constant
분류 has constant value ""Constant
데이터기준일자 has constant value ""Constant
선발인원 is highly overall correlated with 응시현황(명)High correlation
응시현황(명) is highly overall correlated with 선발인원High correlation
구분 is highly overall correlated with 직급High correlation
직급 is highly overall correlated with 구분High correlation
구분 is highly imbalanced (50.8%)Imbalance
직급 is highly imbalanced (52.4%)Imbalance
직류 has 56 (76.7%) missing valuesMissing
응시현황(명) has 3 (4.1%) zerosZeros

Reproduction

Analysis started2023-12-12 05:52:51.729595
Analysis finished2023-12-12 05:52:52.804173
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

해당연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
2022
73 

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 73
100.0%

Length

2023-12-12T14:52:52.878812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:52.980177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 73
100.0%

분류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
원서접수
73 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원서접수 73
100.0%

Length

2023-12-12T14:52:53.118038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:53.221463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원서접수 73
100.0%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size716.0 B
제3회
56 
제5회
6 
제4회
 
5
제1회
 
3
제2회
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
제3회 56
76.7%
제5회 6
 
8.2%
제4회 5
 
6.8%
제1회 3
 
4.1%
제2회 2
 
2.7%
제6회 1
 
1.4%

Length

2023-12-12T14:52:53.368233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:53.543960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제3회 56
76.7%
제5회 6
 
8.2%
제4회 5
 
6.8%
제1회 3
 
4.1%
제2회 2
 
2.7%
제6회 1
 
1.4%

직급
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size716.0 B
9급
57 
연구사
 
5
8급
 
4
고졸9급
 
4
지도사
 
2

Length

Max length4
Median length2
Mean length2.2054795
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row9급
2nd row9급
3rd row9급
4th row8급
5th row8급

Common Values

ValueCountFrequency (%)
9급 57
78.1%
연구사 5
 
6.8%
8급 4
 
5.5%
고졸9급 4
 
5.5%
지도사 2
 
2.7%
7급 1
 
1.4%

Length

2023-12-12T14:52:53.707909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:53.885887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9급 57
78.1%
연구사 5
 
6.8%
8급 4
 
5.5%
고졸9급 4
 
5.5%
지도사 2
 
2.7%
7급 1
 
1.4%

직렬
Text

Distinct39
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-12T14:52:54.099127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length3.9178082
Min length2

Characters and Unicode

Total characters286
Distinct characters63
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)23.3%

Sample

1st row해양수산
2nd row해양수산
3rd row운전
4th row간호
5th row간호
ValueCountFrequency (%)
해양수산 3
 
4.1%
방재안전 3
 
4.1%
건축 3
 
4.1%
일반환경 3
 
4.1%
보건 3
 
4.1%
일반수산 3
 
4.1%
산림자원 3
 
4.1%
사서 3
 
4.1%
일반농업 3
 
4.1%
일반기계 3
 
4.1%
Other values (29) 43
58.9%
2023-12-12T14:52:54.503713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
반 23
 
8.0%
일 23
 
8.0%
산 14
 
4.9%
농 10
 
3.5%
건 9
 
3.1%
지 9
 
3.1%
업 9
 
3.1%
) 8
 
2.8%
전 8
 
2.8%
( 8
 
2.8%
Other values (53) 165
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
94.4%
Close Punctuation 8
 
2.8%
Open Punctuation 8
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 23
 
8.5%
일 23
 
8.5%
산 14
 
5.2%
농 10
 
3.7%
건 9
 
3.3%
지 9
 
3.3%
업 9
 
3.3%
전 8
 
3.0%
수 7
 
2.6%
사 7
 
2.6%
Other values (51) 151
55.9%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
94.4%
Common 16
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 23
 
8.5%
일 23
 
8.5%
산 14
 
5.2%
농 10
 
3.7%
건 9
 
3.3%
지 9
 
3.3%
업 9
 
3.3%
전 8
 
3.0%
수 7
 
2.6%
사 7
 
2.6%
Other values (51) 151
55.9%
Common
ValueCountFrequency (%)
) 8
50.0%
( 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
94.4%
ASCII 16
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
반 23
 
8.5%
일 23
 
8.5%
산 14
 
5.2%
농 10
 
3.7%
건 9
 
3.3%
지 9
 
3.3%
업 9
 
3.3%
전 8
 
3.0%
수 7
 
2.6%
사 7
 
2.6%
Other values (51) 151
55.9%
ASCII
ValueCountFrequency (%)
) 8
50.0%
( 8
50.0%

직류
Text

MISSING 

Distinct15
Distinct (%)88.2%
Missing56
Missing (%)76.7%
Memory size716.0 B
2023-12-12T14:52:54.742558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length3.3529412
Min length2

Characters and Unicode

Total characters57
Distinct characters34
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

Unique13 ?
Unique (%)76.5%

Sample

1st row선박항해
2nd row선박기관
3rd row운전
4th row간호
5th row간호
ValueCountFrequency (%)
간호 2
 
10.5%
원예 2
 
10.5%
학예일반 2
 
10.5%
선박항해 1
 
5.3%
선박기관 1
 
5.3%
운전 1
 
5.3%
축산 1
 
5.3%
농공 1
 
5.3%
농촌생활 1
 
5.3%
미술 1
 
5.3%
Other values (6) 6
31.6%
2023-12-12T14:52:55.144642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
반 5
 
8.8%
일 5
 
8.8%
예 4
 
7.0%
학 3
 
5.3%
농 3
 
5.3%
수 2
 
3.5%
호 2
 
3.5%
산 2
 
3.5%
2
 
3.5%
간 2
 
3.5%
Other values (24) 27
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55
96.5%
Space Separator 2
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 5
 
9.1%
일 5
 
9.1%
예 4
 
7.3%
학 3
 
5.5%
농 3
 
5.5%
수 2
 
3.6%
호 2
 
3.6%
산 2
 
3.6%
간 2
 
3.6%
박 2
 
3.6%
Other values (23) 25
45.5%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55
96.5%
Common 2
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 5
 
9.1%
일 5
 
9.1%
예 4
 
7.3%
학 3
 
5.5%
농 3
 
5.5%
수 2
 
3.6%
호 2
 
3.6%
산 2
 
3.6%
간 2
 
3.6%
박 2
 
3.6%
Other values (23) 25
45.5%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55
96.5%
ASCII 2
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
반 5
 
9.1%
일 5
 
9.1%
예 4
 
7.3%
학 3
 
5.5%
농 3
 
5.5%
수 2
 
3.6%
호 2
 
3.6%
산 2
 
3.6%
간 2
 
3.6%
박 2
 
3.6%
Other values (23) 25
45.5%
ASCII
ValueCountFrequency (%)
2
100.0%

임용예정기관
Categorical

Distinct4
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size716.0 B
제주특별자치도
30 
서귀포시
21 
제주시
21 
도의회
 
1

Length

Max length7
Median length4
Mean length4.9315068
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row제주특별자치도
2nd row제주특별자치도
3rd row서귀포시
4th row제주시
5th row서귀포시

Common Values

ValueCountFrequency (%)
제주특별자치도 30
41.1%
서귀포시 21
28.8%
제주시 21
28.8%
도의회 1
 
1.4%

Length

2023-12-12T14:52:55.310032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:55.482575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 30
41.1%
서귀포시 21
28.8%
제주시 21
28.8%
도의회 1
 
1.4%

선발인원
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9589041
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T14:52:55.611465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10.4
Maximum38
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.4731032
Coefficient of variation (CV)1.6350745
Kurtosis17.897683
Mean3.9589041
Median Absolute Deviation (MAD)1
Skewness4.1456732
Sum289
Variance41.901065
MonotonicityNot monotonic
2023-12-12T14:52:55.769763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 23
31.5%
2 21
28.8%
3 10
13.7%
4 5
 
6.8%
5 4
 
5.5%
7 2
 
2.7%
6 2
 
2.7%
27 1
 
1.4%
38 1
 
1.4%
34 1
 
1.4%
Other values (3) 3
 
4.1%
ValueCountFrequency (%)
1 23
31.5%
2 21
28.8%
3 10
13.7%
4 5
 
6.8%
5 4
 
5.5%
6 2
 
2.7%
7 2
 
2.7%
8 1
 
1.4%
10 1
 
1.4%
11 1
 
1.4%
ValueCountFrequency (%)
38 1
 
1.4%
34 1
 
1.4%
27 1
 
1.4%
11 1
 
1.4%
10 1
 
1.4%
8 1
 
1.4%
7 2
 
2.7%
6 2
 
2.7%
5 4
5.5%
4 5
6.8%

응시현황(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.136986
Minimum0
Maximum761
Zeros3
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T14:52:55.932297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q18
median20
Q339
95-th percentile127.2
Maximum761
Range761
Interquartile range (IQR)31

Descriptive statistics

Standard deviation100.43977
Coefficient of variation (CV)2.225221
Kurtosis37.102201
Mean45.136986
Median Absolute Deviation (MAD)12
Skewness5.632355
Sum3295
Variance10088.148
MonotonicityNot monotonic
2023-12-12T14:52:56.124376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
21 4
 
5.5%
20 3
 
4.1%
0 3
 
4.1%
12 3
 
4.1%
9 3
 
4.1%
4 3
 
4.1%
3 3
 
4.1%
44 2
 
2.7%
5 2
 
2.7%
15 2
 
2.7%
Other values (35) 45
61.6%
ValueCountFrequency (%)
0 3
4.1%
1 1
 
1.4%
2 2
2.7%
3 3
4.1%
4 3
4.1%
5 2
2.7%
6 2
2.7%
7 1
 
1.4%
8 2
2.7%
9 3
4.1%
ValueCountFrequency (%)
761 1
1.4%
303 1
1.4%
296 1
1.4%
132 1
1.4%
124 1
1.4%
119 1
1.4%
107 1
1.4%
86 1
1.4%
82 1
1.4%
64 2
2.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
Minimum2022-11-09 00:00:00
Maximum2022-11-09 00:00:00
2023-12-12T14:52:56.264678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:56.381825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T14:52:52.268888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:52.085327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:52.375526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:52.175516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:52:56.465865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분직급직렬직류임용예정기관선발인원응시현황(명)
구분1.0000.9750.9851.0000.0000.0000.000
직급0.9751.0000.9900.9370.1730.0000.000
직렬0.9850.9901.0000.9810.5910.0000.000
직류1.0000.9370.9811.0000.6890.0000.000
임용예정기관0.0000.1730.5910.6891.0000.0000.356
선발인원0.0000.0000.0000.0000.0001.0000.943
응시현황(명)0.0000.0000.0000.0000.3560.9431.000
2023-12-12T14:52:56.570653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임용예정기관직급구분
임용예정기관1.0000.1060.000
직급0.1061.0000.761
구분0.0000.7611.000
2023-12-12T14:52:56.671891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선발인원응시현황(명)구분직급임용예정기관
선발인원1.0000.7890.0000.0000.000
응시현황(명)0.7891.0000.0000.0000.143
구분0.0000.0001.0000.7610.000
직급0.0000.0000.7611.0000.106
임용예정기관0.0000.1430.0000.1061.000

Missing values

2023-12-12T14:52:52.529989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:52:52.734849image/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

해당연도분류구분직급직렬직류임용예정기관선발인원응시현황(명)데이터기준일자
02022원서접수제1회9급해양수산선박항해제주특별자치도3212022-11-09
12022원서접수제1회9급해양수산선박기관제주특별자치도4162022-11-09
22022원서접수제1회9급운전운전서귀포시21072022-11-09
32022원서접수제2회8급간호간호제주시71242022-11-09
42022원서접수제2회8급간호간호서귀포시3642022-11-09
52022원서접수제3회8급보건진료<NA>제주시1222022-11-09
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