Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory57.0 B

Variable types

Numeric3
Text2
Categorical1

Dataset

Description한국남부발전(주)_연도별 품질명장 선발현황에 대한 데이터로 순번, 현재 소속, 성명, 선발년도, 기수, 재직여부에 관한 데이터를 제공합니다.
Author한국남부발전(주)
URLhttps://www.data.go.kr/data/15124872/fileData.do

Alerts

재직여부 has constant value ""Constant
순번 is highly overall correlated with 선발년도 and 1 other fieldsHigh correlation
선발년도 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
기수 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:52:39.513682
Analysis finished2023-12-12 08:52:40.942631
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T17:52:41.005301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2023-12-12T17:52:41.154033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

현재 소속
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T17:52:41.368752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.3181818
Min length2

Characters and Unicode

Total characters161
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)27.3%

Sample

1st row하동빛드림본부
2nd row하동빛드림본부
3rd row남제주빛드림본부
4th row영월빛드림본부
5th row본사
ValueCountFrequency (%)
하동빛드림본부 5
22.7%
남제주빛드림본부 4
18.2%
신세종빛드림건설본부 3
13.6%
본사 2
 
9.1%
삼척빛드림본부 2
 
9.1%
영월빛드림본부 1
 
4.5%
기술전문센터 1
 
4.5%
안동빛드림본부 1
 
4.5%
신재생빛드림권역사업실 1
 
4.5%
신인천빛드림본부 1
 
4.5%
2023-12-12T17:52:41.712597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
본 20
12.4%
빛 19
11.8%
드 19
11.8%
림 19
11.8%
부 19
11.8%
동 6
 
3.7%
하 5
 
3.1%
신 5
 
3.1%
남 4
 
2.5%
제 4
 
2.5%
Other values (26) 41
25.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 161
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
본 20
12.4%
빛 19
11.8%
드 19
11.8%
림 19
11.8%
부 19
11.8%
동 6
 
3.7%
하 5
 
3.1%
신 5
 
3.1%
남 4
 
2.5%
제 4
 
2.5%
Other values (26) 41
25.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 161
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
본 20
12.4%
빛 19
11.8%
드 19
11.8%
림 19
11.8%
부 19
11.8%
동 6
 
3.7%
하 5
 
3.1%
신 5
 
3.1%
남 4
 
2.5%
제 4
 
2.5%
Other values (26) 41
25.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 161
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
본 20
12.4%
빛 19
11.8%
드 19
11.8%
림 19
11.8%
부 19
11.8%
동 6
 
3.7%
하 5
 
3.1%
신 5
 
3.1%
남 4
 
2.5%
제 4
 
2.5%
Other values (26) 41
25.5%

성명
Text

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T17:52:41.893558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters13
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

Unique9 ?
Unique (%)40.9%

Sample

1st row서00
2nd row김00
3rd row양00
4th row김00
5th row김00
ValueCountFrequency (%)
이00 7
31.8%
김00 4
18.2%
오00 2
 
9.1%
서00 1
 
4.5%
양00 1
 
4.5%
류00 1
 
4.5%
허00 1
 
4.5%
조00 1
 
4.5%
최00 1
 
4.5%
강00 1
 
4.5%
Other values (2) 2
 
9.1%
2023-12-12T17:52:42.192075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44
66.7%
이 7
 
10.6%
김 4
 
6.1%
오 2
 
3.0%
서 1
 
1.5%
양 1
 
1.5%
류 1
 
1.5%
허 1
 
1.5%
조 1
 
1.5%
최 1
 
1.5%
Other values (3) 3
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44
66.7%
Other Letter 22
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 7
31.8%
김 4
18.2%
오 2
 
9.1%
서 1
 
4.5%
양 1
 
4.5%
류 1
 
4.5%
허 1
 
4.5%
조 1
 
4.5%
최 1
 
4.5%
강 1
 
4.5%
Other values (2) 2
 
9.1%
Decimal Number
ValueCountFrequency (%)
0 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44
66.7%
Hangul 22
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 7
31.8%
김 4
18.2%
오 2
 
9.1%
서 1
 
4.5%
양 1
 
4.5%
류 1
 
4.5%
허 1
 
4.5%
조 1
 
4.5%
최 1
 
4.5%
강 1
 
4.5%
Other values (2) 2
 
9.1%
Common
ValueCountFrequency (%)
0 44
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44
66.7%
Hangul 22
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44
100.0%
Hangul
ValueCountFrequency (%)
이 7
31.8%
김 4
18.2%
오 2
 
9.1%
서 1
 
4.5%
양 1
 
4.5%
류 1
 
4.5%
허 1
 
4.5%
조 1
 
4.5%
최 1
 
4.5%
강 1
 
4.5%
Other values (2) 2
 
9.1%

선발년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.8182
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T17:52:42.329062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2017
Q12018.25
median2020.5
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.7898221
Coefficient of variation (CV)0.00088613031
Kurtosis-0.66426154
Mean2019.8182
Median Absolute Deviation (MAD)1
Skewness-0.68676612
Sum44436
Variance3.2034632
MonotonicityIncreasing
2023-12-12T17:52:42.443765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 8
36.4%
2018 3
 
13.6%
2020 3
 
13.6%
2022 3
 
13.6%
2017 2
 
9.1%
2019 2
 
9.1%
2016 1
 
4.5%
ValueCountFrequency (%)
2016 1
 
4.5%
2017 2
 
9.1%
2018 3
 
13.6%
2019 2
 
9.1%
2020 3
 
13.6%
2021 8
36.4%
2022 3
 
13.6%
ValueCountFrequency (%)
2022 3
 
13.6%
2021 8
36.4%
2020 3
 
13.6%
2019 2
 
9.1%
2018 3
 
13.6%
2017 2
 
9.1%
2016 1
 
4.5%

기수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8181818
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T17:52:42.569594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13.25
median5.5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.7898221
Coefficient of variation (CV)0.37147251
Kurtosis-0.66426154
Mean4.8181818
Median Absolute Deviation (MAD)1
Skewness-0.68676612
Sum106
Variance3.2034632
MonotonicityIncreasing
2023-12-12T17:52:42.668348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 8
36.4%
3 3
 
13.6%
5 3
 
13.6%
7 3
 
13.6%
2 2
 
9.1%
4 2
 
9.1%
1 1
 
4.5%
ValueCountFrequency (%)
1 1
 
4.5%
2 2
 
9.1%
3 3
 
13.6%
4 2
 
9.1%
5 3
 
13.6%
6 8
36.4%
7 3
 
13.6%
ValueCountFrequency (%)
7 3
 
13.6%
6 8
36.4%
5 3
 
13.6%
4 2
 
9.1%
3 3
 
13.6%
2 2
 
9.1%
1 1
 
4.5%

재직여부
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
재직중
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재직중 22
100.0%

Length

2023-12-12T17:52:42.785948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:52:42.885054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재직중 22
100.0%

Interactions

2023-12-12T17:52:40.400923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:39.730104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:40.059875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:40.509127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:39.823454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:40.170836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:40.625265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:39.946661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:52:40.285606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:52:42.946623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번현재 소속성명선발년도기수
순번1.0000.6690.4950.9380.872
현재 소속0.6691.0000.0000.5960.370
성명0.4950.0001.0000.6580.776
선발년도0.9380.5960.6581.0001.000
기수0.8720.3700.7761.0001.000
2023-12-12T17:52:43.076069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번선발년도기수
순번1.0000.9720.972
선발년도0.9721.0001.000
기수0.9721.0001.000

Missing values

2023-12-12T17:52:40.790016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:52:40.899256image/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

순번현재 소속성명선발년도기수재직여부
01하동빛드림본부서0020161재직중
12하동빛드림본부김0020172재직중
23남제주빛드림본부양0020172재직중
34영월빛드림본부김0020183재직중
45본사김0020183재직중
56본사김0020183재직중
67신세종빛드림건설본부류0020194재직중
78기술전문센터이0020194재직중
89신세종빛드림건설본부허0020205재직중
910신세종빛드림건설본부오0020205재직중
순번현재 소속성명선발년도기수재직여부
1213하동빛드림본부조0020216재직중
1314안동빛드림본부최0020216재직중
1415신재생빛드림권역사업실이0020216재직중
1516신인천빛드림본부강0020216재직중
1617삼척빛드림본부문0020216재직중
1718남제주빛드림본부이0020216재직중
1819남제주빛드림본부이0020216재직중
1920하동빛드림본부박0020227재직중
2021부산빛드림본부이0020227재직중
2122남제주빛드림본부오0020227재직중