Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory68.7 B

Variable types

Numeric1
Categorical5
Text1
DateTime1

Dataset

Description2022년 하반기 대전광역시 공공체육시설 안전점검 결과(번호, 구분, 시설분류, 체육시설명, 시설담당자, 안전등급 등 포함)입니다.
URLhttps://www.data.go.kr/data/15081383/fileData.do

Alerts

시설담당자 has constant value ""Constant
작성경로 has constant value ""Constant
안전등급 has constant value ""Constant
번호 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
시설분류 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
구분 is highly imbalanced (58.6%)Imbalance
번호 has unique valuesUnique
체육시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:15:19.260013
Analysis finished2023-12-12 12:15:20.010793
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T21:15:20.110306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2023-12-12T21:15:20.264130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
공공
33 
등록
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공 33
91.7%
등록 3
 
8.3%

Length

2023-12-12T21:15:20.413766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:15:20.556643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공 33
91.7%
등록 3
 
8.3%

시설분류
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
테니스장
5 
체육관
5 
축구장
4 
간이운동장
4 
기타시설
3 
Other values (9)
15 

Length

Max length8
Median length7
Mean length4.1111111
Min length3

Unique

Unique4 ?
Unique (%)11.1%

Sample

1st row축구장
2nd row축구장
3rd row축구장
4th row축구장
5th row야구장

Common Values

ValueCountFrequency (%)
테니스장 5
13.9%
체육관 5
13.9%
축구장 4
11.1%
간이운동장 4
11.1%
기타시설 3
8.3%
골프장업 3
8.3%
전천후게이트볼장 2
 
5.6%
수영장 2
 
5.6%
롤러스케이트장 2
 
5.6%
국궁장 2
 
5.6%
Other values (4) 4
11.1%

Length

2023-12-12T21:15:20.715391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
테니스장 5
13.9%
체육관 5
13.9%
축구장 4
11.1%
간이운동장 4
11.1%
기타시설 3
8.3%
골프장업 3
8.3%
전천후게이트볼장 2
 
5.6%
수영장 2
 
5.6%
롤러스케이트장 2
 
5.6%
국궁장 2
 
5.6%
Other values (4) 4
11.1%

체육시설명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T21:15:21.028150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.7222222
Min length5

Characters and Unicode

Total characters278
Distinct characters96
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

Unique36 ?
Unique (%)100.0%

Sample

1st row대전월드컵경기장보조경기장
2nd row대전월드컵경기장
3rd row덕암축구센터
4th row사정축구장
5th row한밭야구장
ValueCountFrequency (%)
대전월드컵경기장보조경기장 1
 
2.8%
대전월드컵경기장 1
 
2.8%
월평궁도장 1
 
2.8%
한밭게이트볼장 1
 
2.8%
전천후게이트볼장 1
 
2.8%
한밭수영장 1
 
2.8%
용운국제수영장 1
 
2.8%
월드컵인라인롤러장 1
 
2.8%
사정인라인스케이트장 1
 
2.8%
지수체육공원궁도장(회덕정 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T21:15:21.754084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 24
 
8.6%
구 10
 
3.6%
한 10
 
3.6%
원 9
 
3.2%
육 9
 
3.2%
체 8
 
2.9%
수 7
 
2.5%
전 7
 
2.5%
기 6
 
2.2%
공 6
 
2.2%
Other values (86) 182
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
97.1%
Close Punctuation 4
 
1.4%
Open Punctuation 4
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 24
 
8.9%
구 10
 
3.7%
한 10
 
3.7%
원 9
 
3.3%
육 9
 
3.3%
체 8
 
3.0%
수 7
 
2.6%
전 7
 
2.6%
기 6
 
2.2%
공 6
 
2.2%
Other values (84) 174
64.4%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
97.1%
Common 8
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 24
 
8.9%
구 10
 
3.7%
한 10
 
3.7%
원 9
 
3.3%
육 9
 
3.3%
체 8
 
3.0%
수 7
 
2.6%
전 7
 
2.6%
기 6
 
2.2%
공 6
 
2.2%
Other values (84) 174
64.4%
Common
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
97.1%
ASCII 8
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 24
 
8.9%
구 10
 
3.7%
한 10
 
3.7%
원 9
 
3.3%
육 9
 
3.3%
체 8
 
3.0%
수 7
 
2.6%
전 7
 
2.6%
기 6
 
2.2%
공 6
 
2.2%
Other values (84) 174
64.4%
ASCII
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

시설담당자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
윤여채
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
윤여채 36
100.0%

Length

2023-12-12T21:15:21.921556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:15:22.025335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
윤여채 36
100.0%

작성경로
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
SFMS
36 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
SFMS 36
100.0%

Length

2023-12-12T21:15:22.146403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:15:22.244645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sfms 36
100.0%

안전등급
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
양호
36 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양호 36
100.0%

Length

2023-12-12T21:15:22.345326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:15:22.442764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양호 36
100.0%

점검일자
Date

Distinct19
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2022-10-12 00:00:00
Maximum2022-12-14 00:00:00
2023-12-12T21:15:22.540054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:22.680390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

Interactions

2023-12-12T21:15:19.578272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:15:22.768260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호구분시설분류체육시설명점검일자
번호1.0000.9430.9351.0000.620
구분0.9431.0001.0001.0000.741
시설분류0.9351.0001.0001.0000.749
체육시설명1.0001.0001.0001.0001.000
점검일자0.6200.7410.7491.0001.000
2023-12-12T21:15:22.891469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설분류
구분1.0000.804
시설분류0.8041.000
2023-12-12T21:15:23.011356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호구분시설분류
번호1.0000.6960.731
구분0.6961.0000.804
시설분류0.7310.8041.000

Missing values

2023-12-12T21:15:19.759567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:15:19.945912image/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

번호구분시설분류체육시설명시설담당자작성경로안전등급점검일자
01공공축구장대전월드컵경기장보조경기장윤여채SFMS양호2022-11-24
12공공축구장대전월드컵경기장윤여채SFMS양호2022-11-24
23공공축구장덕암축구센터윤여채SFMS양호2022-12-07
34공공축구장사정축구장윤여채SFMS양호2022-11-17
45공공야구장한밭야구장윤여채SFMS양호2022-11-30
56공공사이클경기장월평사이클경기장윤여채SFMS양호2022-11-09
67공공테니스장송강체육관윤여채SFMS양호2022-10-28
78공공테니스장한국전력공사(한전전력연구원)윤여채SFMS양호2022-10-12
89공공테니스장한국수력원자력(주)윤여채SFMS양호2022-11-16
910공공테니스장한국원자력안전기술원윤여채SFMS양호2022-11-23
번호구분시설분류체육시설명시설담당자작성경로안전등급점검일자
2627공공국궁장월평궁도장윤여채SFMS양호2022-11-15
2728공공국궁장지수체육공원궁도장(회덕정)윤여채SFMS양호2022-10-28
2829공공양궁장월평양궁장윤여채SFMS양호2022-11-15
2930공공승마장복용승마장윤여채SFMS양호2022-10-26
3031공공기타시설지수체육공원풋살장윤여채SFMS양호2022-11-29
3132공공기타시설지수체육공원론볼장윤여채SFMS양호2022-11-23
3233공공기타시설인공암벽장윤여채SFMS양호2022-11-25
3334등록골프장업금실컨트리클럽윤여채SFMS양호2022-12-06
3435등록골프장업유성컨트리클럽윤여채SFMS양호2022-12-05
3536등록골프장업대덕연구개발특구복지센터윤여채SFMS양호2022-11-24