Overview

Dataset statistics

Number of variables5
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory46.9 B

Variable types

Categorical1
Text2
Numeric2

Dataset

Description시흥도시공사의 전년도 채용 통계입니다. 해당연도를 비롯한 채용개요 및 인원, 지원인원 및 경쟁률에 대한 데이터를 포함합니다.
Author시흥도시공사
URLhttps://www.data.go.kr/data/15098729/fileData.do

Alerts

채용인원 is highly overall correlated with 지원인원High correlation
지원인원 is highly overall correlated with 채용인원High correlation

Reproduction

Analysis started2024-03-15 00:14:15.506213
Analysis finished2024-03-15 00:14:17.375708
Duration1.87 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

해당연도
Categorical

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size392.0 B
2020
10 
2021
9 
2022
8 
2023
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2020 10
30.3%
2021 9
27.3%
2022 8
24.2%
2023 6
18.2%

Length

2024-03-15T09:14:17.588043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:14:17.927576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10
30.3%
2021 9
27.3%
2022 8
24.2%
2023 6
18.2%

채용개요
Text

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T09:14:18.578187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length21
Mean length16.666667
Min length8

Characters and Unicode

Total characters550
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)93.9%

Sample

1st row제1회 직원 채용
2nd row제1회 정원외 직원 및 대체인력 채용
3rd row상반기 정기 직원 채용
4th row제1회 기간제 수시 채용
5th row하반기 정기 직원 채용
ValueCountFrequency (%)
채용 27
17.6%
직원 22
14.4%
대체인력 13
8.5%
공개경쟁 12
 
7.8%
제1회 12
 
7.8%
및 10
 
6.5%
정원외 7
 
4.6%
제2회 7
 
4.6%
경력경쟁 7
 
4.6%
제4회 6
 
3.9%
Other values (17) 30
19.6%
2024-03-15T09:14:19.451862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
21.8%
제 38
 
6.9%
채 33
 
6.0%
용 33
 
6.0%
회 32
 
5.8%
경 32
 
5.8%
원 29
 
5.3%
쟁 24
 
4.4%
력 23
 
4.2%
직 22
 
4.0%
Other values (27) 164
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 394
71.6%
Space Separator 120
 
21.8%
Decimal Number 32
 
5.8%
Other Punctuation 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 38
 
9.6%
채 33
 
8.4%
용 33
 
8.4%
회 32
 
8.1%
경 32
 
8.1%
원 29
 
7.4%
쟁 24
 
6.1%
력 23
 
5.8%
직 22
 
5.6%
개 17
 
4.3%
Other values (18) 111
28.2%
Decimal Number
ValueCountFrequency (%)
1 12
37.5%
2 7
21.9%
3 6
18.8%
4 6
18.8%
5 1
 
3.1%
Space Separator
ValueCountFrequency (%)
120
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 394
71.6%
Common 156
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 38
 
9.6%
채 33
 
8.4%
용 33
 
8.4%
회 32
 
8.1%
경 32
 
8.1%
원 29
 
7.4%
쟁 24
 
6.1%
력 23
 
5.8%
직 22
 
5.6%
개 17
 
4.3%
Other values (18) 111
28.2%
Common
ValueCountFrequency (%)
120
76.9%
1 12
 
7.7%
2 7
 
4.5%
3 6
 
3.8%
4 6
 
3.8%
, 2
 
1.3%
( 1
 
0.6%
) 1
 
0.6%
5 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 394
71.6%
ASCII 156
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
76.9%
1 12
 
7.7%
2 7
 
4.5%
3 6
 
3.8%
4 6
 
3.8%
, 2
 
1.3%
( 1
 
0.6%
) 1
 
0.6%
5 1
 
0.6%
Hangul
ValueCountFrequency (%)
제 38
 
9.6%
채 33
 
8.4%
용 33
 
8.4%
회 32
 
8.1%
경 32
 
8.1%
원 29
 
7.4%
쟁 24
 
6.1%
력 23
 
5.8%
직 22
 
5.6%
개 17
 
4.3%
Other values (18) 111
28.2%

채용인원
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9393939
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T09:14:19.825594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q312
95-th percentile19.2
Maximum21
Range20
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9473702
Coefficient of variation (CV)0.74909624
Kurtosis-0.40123112
Mean7.9393939
Median Absolute Deviation (MAD)4
Skewness0.79791694
Sum262
Variance35.371212
MonotonicityNot monotonic
2024-03-15T09:14:20.221109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 4
12.1%
2 3
9.1%
8 3
9.1%
5 3
9.1%
3 3
9.1%
1 3
9.1%
21 2
 
6.1%
13 2
 
6.1%
12 2
 
6.1%
9 1
 
3.0%
Other values (7) 7
21.2%
ValueCountFrequency (%)
1 3
9.1%
2 3
9.1%
3 3
9.1%
4 4
12.1%
5 3
9.1%
6 1
 
3.0%
7 1
 
3.0%
8 3
9.1%
9 1
 
3.0%
10 1
 
3.0%
ValueCountFrequency (%)
21 2
6.1%
18 1
 
3.0%
17 1
 
3.0%
16 1
 
3.0%
14 1
 
3.0%
13 2
6.1%
12 2
6.1%
10 1
 
3.0%
9 1
 
3.0%
8 3
9.1%

지원인원
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.393939
Minimum1
Maximum239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T09:14:20.456071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q18
median17
Q369
95-th percentile222.6
Maximum239
Range238
Interquartile range (IQR)61

Descriptive statistics

Standard deviation69.516158
Coefficient of variation (CV)1.3267977
Kurtosis1.9229739
Mean52.393939
Median Absolute Deviation (MAD)14
Skewness1.7059855
Sum1729
Variance4832.4962
MonotonicityNot monotonic
2024-03-15T09:14:20.862545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4 2
 
6.1%
3 2
 
6.1%
9 2
 
6.1%
24 1
 
3.0%
1 1
 
3.0%
69 1
 
3.0%
5 1
 
3.0%
14 1
 
3.0%
40 1
 
3.0%
12 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 2
6.1%
4 2
6.1%
5 1
3.0%
6 1
3.0%
8 1
3.0%
9 2
6.1%
10 1
3.0%
11 1
3.0%
ValueCountFrequency (%)
239 1
3.0%
228 1
3.0%
219 1
3.0%
169 1
3.0%
128 1
3.0%
107 1
3.0%
97 1
3.0%
76 1
3.0%
69 1
3.0%
49 1
3.0%

경쟁률
Text

Distinct26
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T09:14:21.608694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.969697
Min length4

Characters and Unicode

Total characters164
Distinct characters12
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

Unique20 ?
Unique (%)60.6%

Sample

1st row1.7:1
2nd row1.8:1
3rd row8:01
4th row1.4:1
5th row5.7:1
ValueCountFrequency (%)
1.5:1 3
 
9.1%
3.5:1 2
 
6.1%
5.6:1 2
 
6.1%
4:01 2
 
6.1%
5.7:1 2
 
6.1%
2:01 2
 
6.1%
0.7:1 1
 
3.0%
1.7:1 1
 
3.0%
7:01 1
 
3.0%
17.3:1 1
 
3.0%
Other values (16) 16
48.5%
2024-03-15T09:14:22.718033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 51
31.1%
: 33
20.1%
. 24
14.6%
0 12
 
7.3%
5 11
 
6.7%
3 9
 
5.5%
7 7
 
4.3%
2 4
 
2.4%
6 4
 
2.4%
8 4
 
2.4%
Other values (2) 5
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107
65.2%
Other Punctuation 57
34.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51
47.7%
0 12
 
11.2%
5 11
 
10.3%
3 9
 
8.4%
7 7
 
6.5%
2 4
 
3.7%
6 4
 
3.7%
8 4
 
3.7%
4 3
 
2.8%
9 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
: 33
57.9%
. 24
42.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 51
31.1%
: 33
20.1%
. 24
14.6%
0 12
 
7.3%
5 11
 
6.7%
3 9
 
5.5%
7 7
 
4.3%
2 4
 
2.4%
6 4
 
2.4%
8 4
 
2.4%
Other values (2) 5
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 51
31.1%
: 33
20.1%
. 24
14.6%
0 12
 
7.3%
5 11
 
6.7%
3 9
 
5.5%
7 7
 
4.3%
2 4
 
2.4%
6 4
 
2.4%
8 4
 
2.4%
Other values (2) 5
 
3.0%

Interactions

2024-03-15T09:14:16.379612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:14:15.849350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:14:16.659942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:14:16.126157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:14:22.976274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
해당연도채용개요채용인원지원인원경쟁률
해당연도1.0000.8780.5070.0000.632
채용개요0.8781.0000.8801.0000.989
채용인원0.5070.8801.0000.7060.871
지원인원0.0001.0000.7061.0000.970
경쟁률0.6320.9890.8710.9701.000
2024-03-15T09:14:23.240758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채용인원지원인원해당연도
채용인원1.0000.8280.119
지원인원0.8281.0000.000
해당연도0.1190.0001.000

Missing values

2024-03-15T09:14:16.938771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:14:17.255808image/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

해당연도채용개요채용인원지원인원경쟁률
02023제1회 직원 채용14241.7:1
12023제1회 정원외 직원 및 대체인력 채용591.8:1
22023상반기 정기 직원 채용211698:01
32023제1회 기간제 수시 채용8111.4:1
42023하반기 정기 직원 채용17975.7:1
52023수시 직원 채용13493.8:1
62022제1회 직원 채용시험1823913.3:1
72022제1회 정원외 직원 및 제1회 대체인력 공개경쟁 및 제한경쟁채용8162:01
82022제2회 직원 경력경쟁 채용12423.5:1
92022제3회 직원 채용1212810.6:1
해당연도채용개요채용인원지원인원경쟁률
232020제2회 대체인력 공개경쟁 채용3103.3:1
242020제1회 직원 공개경쟁 채용2122811:01
252020제1회 대체인력 공개경쟁 채용11212:01
262020제2회 직원 경력경쟁 채용7405.7:1
272020제3회 직원 경력경쟁 채용4143.5:1
282020제3회 대체인력 공개경쟁 채용351.67:1
292020제4회 대체인력 공개경쟁 채용691.5:1
302020제4회 직원 공개경쟁 채용46917.3:1
312020제2회 정원외 인력 공개경쟁 채용133:01
322020제1회 정원외 인력 공개경쟁 채용244:01