Overview

Dataset statistics

Number of variables3
Number of observations32
Missing cells25
Missing cells (%)26.0%
Duplicate rows1
Duplicate rows (%)3.1%
Total size in memory900.0 B
Average record size in memory28.1 B

Variable types

Text1
Categorical2

Dataset

Description가평군시설관리공단 경영평가 등급결과에 대한 데이터로 연도별( 2016년 ~ 2022년) 경영평가 등급 결과 자료 입니다.
Author가평군시설관리공단
URLhttps://www.data.go.kr/data/15121526/fileData.do

Alerts

Dataset has 1 (3.1%) duplicate rowsDuplicates
데이터기준일자 is highly overall correlated with 평가등급High correlation
평가등급 is highly overall correlated with 데이터기준일자High correlation
평가(실적)연도 has 25 (78.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 03:38:39.248812
Analysis finished2023-12-12 03:38:39.564150
Duration0.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

평가(실적)연도
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing25
Missing (%)78.1%
Memory size388.0 B
2023-12-12T12:38:39.704170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row2022(2021)
2nd row2021(2020)
3rd row2020(2019)
4th row2019(2018)
5th row2018(2017)
ValueCountFrequency (%)
2022(2021 1
14.3%
2021(2020 1
14.3%
2020(2019 1
14.3%
2019(2018 1
14.3%
2018(2017 1
14.3%
2017(2016 1
14.3%
2016(2015 1
14.3%
2023-12-12T12:38:40.144028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 20
28.6%
0 16
22.9%
1 11
15.7%
( 7
 
10.0%
) 7
 
10.0%
9 2
 
2.9%
8 2
 
2.9%
7 2
 
2.9%
6 2
 
2.9%
5 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
80.0%
Open Punctuation 7
 
10.0%
Close Punctuation 7
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 20
35.7%
0 16
28.6%
1 11
19.6%
9 2
 
3.6%
8 2
 
3.6%
7 2
 
3.6%
6 2
 
3.6%
5 1
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 20
28.6%
0 16
22.9%
1 11
15.7%
( 7
 
10.0%
) 7
 
10.0%
9 2
 
2.9%
8 2
 
2.9%
7 2
 
2.9%
6 2
 
2.9%
5 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 20
28.6%
0 16
22.9%
1 11
15.7%
( 7
 
10.0%
) 7
 
10.0%
9 2
 
2.9%
8 2
 
2.9%
7 2
 
2.9%
6 2
 
2.9%
5 1
 
1.4%

평가등급
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
25 
나
4 
다
3 

Length

Max length4
Median length4
Mean length3.34375
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row나
2nd row다
3rd row나
4th row나
5th row다

Common Values

ValueCountFrequency (%)
<NA> 25
78.1%
나 4
 
12.5%
다 3
 
9.4%

Length

2023-12-12T12:38:40.354612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:38:40.527179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
78.1%
나 4
 
12.5%
다 3
 
9.4%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
25 
2023-08-31
7 

Length

Max length10
Median length4
Mean length5.3125
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-31
2nd row2023-08-31
3rd row2023-08-31
4th row2023-08-31
5th row2023-08-31

Common Values

ValueCountFrequency (%)
<NA> 25
78.1%
2023-08-31 7
 
21.9%

Length

2023-12-12T12:38:40.701519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:38:40.844494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
78.1%
2023-08-31 7
 
21.9%

Correlations

2023-12-12T12:38:40.934978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평가(실적)연도평가등급
평가(실적)연도1.0001.000
평가등급1.0001.000
2023-12-12T12:38:41.057254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일자평가등급
데이터기준일자1.0001.000
평가등급1.0001.000
2023-12-12T12:38:41.177283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평가등급데이터기준일자
평가등급1.0001.000
데이터기준일자1.0001.000

Missing values

2023-12-12T12:38:39.391024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:38:39.513476image/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

평가(실적)연도평가등급데이터기준일자
02022(2021)나2023-08-31
12021(2020)다2023-08-31
22020(2019)나2023-08-31
32019(2018)나2023-08-31
42018(2017)다2023-08-31
52017(2016)다2023-08-31
62016(2015)나2023-08-31
7<NA><NA><NA>
8<NA><NA><NA>
9<NA><NA><NA>
평가(실적)연도평가등급데이터기준일자
22<NA><NA><NA>
23<NA><NA><NA>
24<NA><NA><NA>
25<NA><NA><NA>
26<NA><NA><NA>
27<NA><NA><NA>
28<NA><NA><NA>
29<NA><NA><NA>
30<NA><NA><NA>
31<NA><NA><NA>

Duplicate rows

Most frequently occurring

평가(실적)연도평가등급데이터기준일자# duplicates
0<NA><NA><NA>25