Overview

Dataset statistics

Number of variables6
Number of observations220
Missing cells146
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory50.6 B

Variable types

Categorical4
Numeric1
Text1

Dataset

Description한국에너지공단에서 집단에너지 사업자를 대상으로 수행하는 열공급시설 검사(열원시설, 열수송시설의 사용전 검사 및 정기검사) 관련 연도별, 지역본부별, 검사검수, 검사길이 등에 대한 데이터
Author한국에너지공단
URLhttps://www.data.go.kr/data/15086267/fileData.do

Alerts

열공급시설 검사건수(건) has 34 (15.5%) missing valuesMissing
열수송관 검사길이(km) has 112 (50.9%) missing valuesMissing
열공급시설 검사건수(건) has 5 (2.3%) zerosZeros

Reproduction

Analysis started2024-03-14 23:53:35.778591
Analysis finished2024-03-14 23:53:37.276184
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023
44 
2022
44 
2021
44 
2020
44 
2019
44 

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 (%)
2023 44
20.0%
2022 44
20.0%
2021 44
20.0%
2020 44
20.0%
2019 44
20.0%

Length

2024-03-15T08:53:37.482991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:53:37.848253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 44
20.0%
2022 44
20.0%
2021 44
20.0%
2020 44
20.0%
2019 44
20.0%

열공급시설 검사종류
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
사용전 검사
110 
정기검사
110 

Length

Max length6
Median length5
Mean length5
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사용전 검사
2nd row사용전 검사
3rd row사용전 검사
4th row사용전 검사
5th row사용전 검사

Common Values

ValueCountFrequency (%)
사용전 검사 110
50.0%
정기검사 110
50.0%

Length

2024-03-15T08:53:38.142516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:53:38.407232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사용전 110
33.3%
검사 110
33.3%
정기검사 110
33.3%

열공급시설 구분
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
열원시설
110 
열수송시설
110 

Length

Max length5
Median length4.5
Mean length4.5
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
열원시설 110
50.0%
열수송시설 110
50.0%

Length

2024-03-15T08:53:38.817970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:53:39.145814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
열원시설 110
50.0%
열수송시설 110
50.0%

지역본부
Categorical

Distinct11
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
서울
20 
부산울산
20 
대구경북
20 
인천
20 
광주전남
20 
Other values (6)
120 

Length

Max length4
Median length2
Mean length2.9090909
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산울산
3rd row대구경북
4th row인천
5th row광주전남

Common Values

ValueCountFrequency (%)
서울 20
9.1%
부산울산 20
9.1%
대구경북 20
9.1%
인천 20
9.1%
광주전남 20
9.1%
대전충남 20
9.1%
세종충북 20
9.1%
경기 20
9.1%
강원 20
9.1%
전북 20
9.1%

Length

2024-03-15T08:53:39.705283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 20
9.1%
부산울산 20
9.1%
대구경북 20
9.1%
인천 20
9.1%
광주전남 20
9.1%
대전충남 20
9.1%
세종충북 20
9.1%
경기 20
9.1%
강원 20
9.1%
전북 20
9.1%

열공급시설 검사건수(건)
Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)12.9%
Missing34
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean8.9731183
Minimum0
Maximum68
Zeros5
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T08:53:40.073611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q311
95-th percentile32.75
Maximum68
Range68
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.673247
Coefficient of variation (CV)1.1894691
Kurtosis10.77879
Mean8.9731183
Median Absolute Deviation (MAD)4
Skewness2.9567538
Sum1669
Variance113.91819
MonotonicityNot monotonic
2024-03-15T08:53:40.466778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5 32
14.5%
1 26
11.8%
3 21
9.5%
11 14
 
6.4%
12 13
 
5.9%
6 11
 
5.0%
9 9
 
4.1%
2 8
 
3.6%
10 7
 
3.2%
17 7
 
3.2%
Other values (14) 38
17.3%
(Missing) 34
15.5%
ValueCountFrequency (%)
0 5
 
2.3%
1 26
11.8%
2 8
 
3.6%
3 21
9.5%
4 3
 
1.4%
5 32
14.5%
6 11
 
5.0%
7 5
 
2.3%
8 5
 
2.3%
9 9
 
4.1%
ValueCountFrequency (%)
68 1
 
0.5%
64 1
 
0.5%
56 1
 
0.5%
48 1
 
0.5%
34 2
 
0.9%
33 4
1.8%
32 2
 
0.9%
31 3
1.4%
17 7
3.2%
16 3
1.4%

열수송관 검사길이(km)
Text

MISSING 

Distinct106
Distinct (%)98.1%
Missing112
Missing (%)50.9%
Memory size1.8 KiB
2024-03-15T08:53:41.567574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.4259259
Min length1

Characters and Unicode

Total characters478
Distinct characters13
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

Unique104 ?
Unique (%)96.3%

Sample

1st row8.1
2nd row24.1
3rd row1.1
4th row40.2
5th row5.1
ValueCountFrequency (%)
5.8 3
 
2.8%
3.7 2
 
1.9%
15.9 1
 
0.9%
1.7 1
 
0.9%
476.7 1
 
0.9%
1246.2 1
 
0.9%
321.2 1
 
0.9%
1207.8 1
 
0.9%
3.2 1
 
0.9%
1.3 1
 
0.9%
Other values (95) 95
88.0%
2024-03-15T08:53:43.112998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 101
21.1%
1 60
12.6%
2 53
11.1%
4 36
 
7.5%
3 35
 
7.3%
7 34
 
7.1%
5 31
 
6.5%
8 30
 
6.3%
6 27
 
5.6%
0 24
 
5.0%
Other values (3) 47
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 353
73.8%
Other Punctuation 103
 
21.5%
Space Separator 22
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 60
17.0%
2 53
15.0%
4 36
10.2%
3 35
9.9%
7 34
9.6%
5 31
8.8%
8 30
8.5%
6 27
7.6%
0 24
 
6.8%
9 23
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 101
98.1%
, 2
 
1.9%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 101
21.1%
1 60
12.6%
2 53
11.1%
4 36
 
7.5%
3 35
 
7.3%
7 34
 
7.1%
5 31
 
6.5%
8 30
 
6.3%
6 27
 
5.6%
0 24
 
5.0%
Other values (3) 47
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 101
21.1%
1 60
12.6%
2 53
11.1%
4 36
 
7.5%
3 35
 
7.3%
7 34
 
7.1%
5 31
 
6.5%
8 30
 
6.3%
6 27
 
5.6%
0 24
 
5.0%
Other values (3) 47
9.8%

Interactions

2024-03-15T08:53:36.108632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:53:43.338799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도열공급시설 검사종류열공급시설 구분지역본부열공급시설 검사건수(건)
연도1.0000.0000.0000.0000.000
열공급시설 검사종류0.0001.0000.0000.0000.307
열공급시설 구분0.0000.0001.0000.0000.000
지역본부0.0000.0000.0001.0000.745
열공급시설 검사건수(건)0.0000.3070.0000.7451.000
2024-03-15T08:53:43.514345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역본부열공급시설 검사종류열공급시설 구분
연도1.0000.0000.0000.000
지역본부0.0001.0000.0000.000
열공급시설 검사종류0.0000.0001.0000.000
열공급시설 구분0.0000.0000.0001.000
2024-03-15T08:53:43.802721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
열공급시설 검사건수(건)연도열공급시설 검사종류열공급시설 구분지역본부
열공급시설 검사건수(건)1.0000.0000.3240.0000.487
연도0.0001.0000.0000.0000.000
열공급시설 검사종류0.3240.0001.0000.0000.000
열공급시설 구분0.0000.0000.0001.0000.000
지역본부0.4870.0000.0000.0001.000

Missing values

2024-03-15T08:53:36.467050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:53:36.839011image/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.
2024-03-15T08:53:37.130423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도열공급시설 검사종류열공급시설 구분지역본부열공급시설 검사건수(건)열수송관 검사길이(km)
02023사용전 검사열원시설서울1<NA>
12023사용전 검사열원시설부산울산1<NA>
22023사용전 검사열원시설대구경북0<NA>
32023사용전 검사열원시설인천1<NA>
42023사용전 검사열원시설광주전남0<NA>
52023사용전 검사열원시설대전충남1<NA>
62023사용전 검사열원시설세종충북0<NA>
72023사용전 검사열원시설경기2<NA>
82023사용전 검사열원시설강원0<NA>
92023사용전 검사열원시설전북2<NA>
연도열공급시설 검사종류열공급시설 구분지역본부열공급시설 검사건수(건)열수송관 검사길이(km)
2102019정기검사열수송시설부산울산15304.4
2112019정기검사열수송시설대구경북9475
2122019정기검사열수송시설인천5731.8
2132019정기검사열수송시설광주전남12272
2142019정기검사열수송시설대전충남10362.5
2152019정기검사열수송시설세종충북6507.6
2162019정기검사열수송시설경기314975.1
2172019정기검사열수송시설강원181.8
2182019정기검사열수송시설전북5106.7
2192019정기검사열수송시설경남3187