Overview

Dataset statistics

Number of variables6
Number of observations104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory52.3 B

Variable types

Numeric3
Categorical2
Text1

Dataset

Description한국서부발전 채용정보입니다. 제공데이터는 년도별 구분별 전형,선발인원,지원인원,경쟁률 입니다. - 데이터 예) 2021,상반기 대졸수준(사무),일반,3,1309,436.3:1
URLhttps://www.data.go.kr/data/15019271/fileData.do

Alerts

선발인원 is highly overall correlated with 지원인원High correlation
지원인원 is highly overall correlated with 선발인원High correlation

Reproduction

Analysis started2023-12-11 23:23:38.820905
Analysis finished2023-12-11 23:23:40.050139
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct10
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.8269
Minimum2014
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T08:23:40.099633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2015
Q12018
median2021
Q32022
95-th percentile2023
Maximum2023
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6197247
Coefficient of variation (CV)0.0012970045
Kurtosis-0.73639009
Mean2019.8269
Median Absolute Deviation (MAD)2
Skewness-0.65984159
Sum210062
Variance6.8629574
MonotonicityDecreasing
2023-12-12T08:23:40.192429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2021 26
25.0%
2022 17
16.3%
2023 15
14.4%
2018 8
 
7.7%
2017 8
 
7.7%
2020 7
 
6.7%
2019 7
 
6.7%
2016 7
 
6.7%
2015 6
 
5.8%
2014 3
 
2.9%
ValueCountFrequency (%)
2014 3
 
2.9%
2015 6
 
5.8%
2016 7
 
6.7%
2017 8
 
7.7%
2018 8
 
7.7%
2019 7
 
6.7%
2020 7
 
6.7%
2021 26
25.0%
2022 17
16.3%
2023 15
14.4%
ValueCountFrequency (%)
2023 15
14.4%
2022 17
16.3%
2021 26
25.0%
2020 7
 
6.7%
2019 7
 
6.7%
2018 8
 
7.7%
2017 8
 
7.7%
2016 7
 
6.7%
2015 6
 
5.8%
2014 3
 
2.9%

구분
Categorical

Distinct33
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size964.0 B
상반기 대졸수준
12 
하반기 대졸수준
12 
상반기 대졸수준(사무)
8 
상반기 대졸수준(기계)
8 
상반기 대졸수준(전기)
7 
Other values (28)
57 

Length

Max length15
Median length13
Mean length9.3269231
Min length2

Unique

Unique16 ?
Unique (%)15.4%

Sample

1st row상반기 대졸수준(사무)
2nd row상반기 대졸수준(사무)
3rd row상반기 대졸수준(사무)
4th row상반기 대졸수준(기계)
5th row상반기 대졸수준(기계)

Common Values

ValueCountFrequency (%)
상반기 대졸수준 12
 
11.5%
하반기 대졸수준 12
 
11.5%
상반기 대졸수준(사무) 8
 
7.7%
상반기 대졸수준(기계) 8
 
7.7%
상반기 대졸수준(전기) 7
 
6.7%
대졸수준 7
 
6.7%
상반기 대졸수준(화학) 5
 
4.8%
고졸수준 4
 
3.8%
하반기 고졸수준 4
 
3.8%
기타 4
 
3.8%
Other values (23) 33
31.7%

Length

2023-12-12T08:23:40.301443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상반기 50
27.6%
대졸수준 31
17.1%
하반기 27
14.9%
대졸수준(사무 10
 
5.5%
대졸수준(기계 10
 
5.5%
고졸수준 8
 
4.4%
대졸수준(전기 8
 
4.4%
대졸수준(ict 5
 
2.8%
대졸수준(화학 5
 
2.8%
기타 4
 
2.2%
Other values (17) 23
12.7%

전형
Categorical

Distinct18
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size964.0 B
일반
29 
보훈
16 
일반전형
12 
장애
9 
보훈전형
8 
Other values (13)
30 

Length

Max length12
Median length2
Mean length3.6153846
Min length2

Unique

Unique7 ?
Unique (%)6.7%

Sample

1st row일반
2nd row보훈
3rd row장애
4th row일반
5th row보훈

Common Values

ValueCountFrequency (%)
일반 29
27.9%
보훈 16
15.4%
일반전형 12
11.5%
장애 9
 
8.7%
보훈전형 8
 
7.7%
장애인전형 8
 
7.7%
<NA> 5
 
4.8%
채용형인턴(일반전형) 4
 
3.8%
회계사 2
 
1.9%
채용형인턴(장애인전형) 2
 
1.9%
Other values (8) 9
 
8.7%

Length

2023-12-12T08:23:40.408782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반 29
27.9%
보훈 16
15.4%
일반전형 12
11.5%
장애 9
 
8.7%
보훈전형 8
 
7.7%
장애인전형 8
 
7.7%
na 5
 
4.8%
채용형인턴(일반전형 4
 
3.8%
채용형인턴(보훈전형 2
 
1.9%
채용형인턴(장애인전형 2
 
1.9%
Other values (8) 9
 
8.7%

선발인원
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.759615
Minimum1
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T08:23:40.511766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310.25
95-th percentile54.95
Maximum83
Range82
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation17.956525
Coefficient of variation (CV)1.6688817
Kurtosis5.6518723
Mean10.759615
Median Absolute Deviation (MAD)2
Skewness2.4702755
Sum1119
Variance322.4368
MonotonicityNot monotonic
2023-12-12T08:23:40.620107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 33
31.7%
2 14
13.5%
3 11
 
10.6%
5 7
 
6.7%
4 5
 
4.8%
12 3
 
2.9%
15 3
 
2.9%
9 3
 
2.9%
6 2
 
1.9%
72 1
 
1.0%
Other values (22) 22
21.2%
ValueCountFrequency (%)
1 33
31.7%
2 14
13.5%
3 11
 
10.6%
4 5
 
4.8%
5 7
 
6.7%
6 2
 
1.9%
7 1
 
1.0%
8 1
 
1.0%
9 3
 
2.9%
10 1
 
1.0%
ValueCountFrequency (%)
83 1
1.0%
77 1
1.0%
72 1
1.0%
70 1
1.0%
57 1
1.0%
56 1
1.0%
49 1
1.0%
48 1
1.0%
40 1
1.0%
38 1
1.0%

지원인원
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean926.63462
Minimum3
Maximum12175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T08:23:40.735624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.15
Q116.75
median49.5
Q3656
95-th percentile5684.85
Maximum12175
Range12172
Interquartile range (IQR)639.25

Descriptive statistics

Standard deviation2039.2822
Coefficient of variation (CV)2.2007403
Kurtosis11.13368
Mean926.63462
Median Absolute Deviation (MAD)42.5
Skewness3.1461566
Sum96370
Variance4158671.8
MonotonicityNot monotonic
2023-12-12T08:23:40.881613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 4
 
3.8%
7 3
 
2.9%
9 3
 
2.9%
11 3
 
2.9%
5 3
 
2.9%
8 3
 
2.9%
203 2
 
1.9%
22 2
 
1.9%
17 2
 
1.9%
32 2
 
1.9%
Other values (72) 77
74.0%
ValueCountFrequency (%)
3 2
1.9%
4 1
 
1.0%
5 3
2.9%
6 1
 
1.0%
7 3
2.9%
8 3
2.9%
9 3
2.9%
10 1
 
1.0%
11 3
2.9%
13 1
 
1.0%
ValueCountFrequency (%)
12175 1
1.0%
8139 1
1.0%
7955 1
1.0%
6135 1
1.0%
5794 1
1.0%
5715 1
1.0%
5514 1
1.0%
4834 1
1.0%
4179 1
1.0%
3404 1
1.0%

경쟁률
Text

Distinct86
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size964.0 B
2023-12-12T08:23:41.128139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.9038462
Min length5

Characters and Unicode

Total characters614
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

Unique73 ?
Unique (%)70.2%

Sample

1st row198.8:1
2nd row32.0:1
3rd row56.0:1
4th row128.0:1
5th row11.0:1
ValueCountFrequency (%)
7.0:1 4
 
3.8%
8.0:1 3
 
2.9%
3.0:1 3
 
2.9%
11.0:1 3
 
2.9%
15.0:1 2
 
1.9%
4.1:1 2
 
1.9%
7.3:1 2
 
1.9%
4.0:1 2
 
1.9%
4.3:1 2
 
1.9%
5.0:1 2
 
1.9%
Other values (76) 79
76.0%
2023-12-12T08:23:41.529051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 151
24.6%
. 104
16.9%
: 104
16.9%
0 61
9.9%
6 30
 
4.9%
5 29
 
4.7%
2 29
 
4.7%
3 26
 
4.2%
4 24
 
3.9%
7 23
 
3.7%
Other values (2) 33
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 406
66.1%
Other Punctuation 208
33.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 151
37.2%
0 61
15.0%
6 30
 
7.4%
5 29
 
7.1%
2 29
 
7.1%
3 26
 
6.4%
4 24
 
5.9%
7 23
 
5.7%
8 23
 
5.7%
9 10
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 104
50.0%
: 104
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 151
24.6%
. 104
16.9%
: 104
16.9%
0 61
9.9%
6 30
 
4.9%
5 29
 
4.7%
2 29
 
4.7%
3 26
 
4.2%
4 24
 
3.9%
7 23
 
3.7%
Other values (2) 33
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 151
24.6%
. 104
16.9%
: 104
16.9%
0 61
9.9%
6 30
 
4.9%
5 29
 
4.7%
2 29
 
4.7%
3 26
 
4.2%
4 24
 
3.9%
7 23
 
3.7%
Other values (2) 33
 
5.4%

Interactions

2023-12-12T08:23:39.581125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.107343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.330652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.673638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.178554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.404992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.763125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.250441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:39.481823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:23:41.644115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분전형선발인원지원인원경쟁률
년도1.0000.6850.7570.6240.4110.772
구분0.6851.0000.8200.0000.0000.000
전형0.7570.8201.0000.5450.5370.666
선발인원0.6240.0000.5451.0000.8530.999
지원인원0.4110.0000.5370.8531.0001.000
경쟁률0.7720.0000.6660.9991.0001.000
2023-12-12T08:23:41.758600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분전형
구분1.0000.351
전형0.3511.000
2023-12-12T08:23:42.047662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도선발인원지원인원구분전형
년도1.000-0.477-0.1770.2420.462
선발인원-0.4771.0000.7380.0000.237
지원인원-0.1770.7381.0000.0000.242
구분0.2420.0000.0001.0000.351
전형0.4620.2370.2420.3511.000

Missing values

2023-12-12T08:23:39.881860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:23:40.012388image/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

년도구분전형선발인원지원인원경쟁률
02023상반기 대졸수준(사무)일반61193198.8:1
12023상반기 대졸수준(사무)보훈13232.0:1
22023상반기 대졸수준(사무)장애15656.0:1
32023상반기 대졸수준(기계)일반6768128.0:1
42023상반기 대졸수준(기계)보훈11111.0:1
52023상반기 대졸수준(기계)장애166.0:1
62023상반기 대졸수준(전기)일반121825152.1:1
72023상반기 대졸수준(전기)보훈2168.0:1
82023상반기 대졸수준(전기)장애11515.0:1
92023상반기 대졸수준(화학)일반1266266.0:1
년도구분전형선발인원지원인원경쟁률
942016회계사회계사2147.0:1
952015기타정보보안전문가25829.0:1
962015대졸수준채용형인턴(일반전형)49340069.4:1
972015대졸수준채용형인턴(보훈전형)8354.4:1
982015대졸수준채용형인턴(장애인전형)5418.2:1
992015고졸수준채용형인턴(일반전형)212103100.1:1
1002015기타기술전문원33511.7:1
1012014대졸수준신입사원40340485.1:1
1022014기타회계사11919.0:1
1032014기타변호사155.0:1