Overview

Dataset statistics

Number of variables4
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory37.4 B

Variable types

Categorical2
Numeric2

Dataset

Description한국전기안전공사에서 제공하는 장소별(발전소,변전소, 송배전선로, 공장, 작업장, 공사장, 공공시설(물), 통신주, 가로등주, 관공서, 군부대, 학교, 종교, 의료, 요식, 유흥, 숙박, 유통, 판매, 빌딩, 오피스텔, 주거시설, 야외, 해상, 기타) 감전사고에 따른 인명피해(사망, 부상)를 확인할 수 있는 데이터입니다. 제공기간은 2018년 ~ 2021년까지 입니다.
URLhttps://www.data.go.kr/data/15119870/fileData.do

Alerts

인명피해(사망) is highly overall correlated with 인명피해(부상)High correlation
인명피해(부상) is highly overall correlated with 인명피해(사망) and 1 other fieldsHigh correlation
장소 is highly overall correlated with 인명피해(부상)High correlation
인명피해(사망) has 26 (46.4%) zerosZeros

Reproduction

Analysis started2023-12-12 06:58:18.935176
Analysis finished2023-12-12 06:58:19.515436
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct4
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size580.0 B
2018
14 
2019
14 
2020
14 
2021
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 14
25.0%
2019 14
25.0%
2020 14
25.0%
2021 14
25.0%

Length

2023-12-12T15:58:19.579654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:58:19.721741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 14
25.0%
2019 14
25.0%
2020 14
25.0%
2021 14
25.0%

장소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
발·변전소
4 
송배전선로
4 
공장/작업장
4 
공사장
4 
공공시설(물)
4 
Other values (9)
36 

Length

Max length8
Median length6.5
Mean length5.7142857
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발·변전소
2nd row송배전선로
3rd row공장/작업장
4th row공사장
5th row공공시설(물)

Common Values

ValueCountFrequency (%)
발·변전소 4
 
7.1%
송배전선로 4
 
7.1%
공장/작업장 4
 
7.1%
공사장 4
 
7.1%
공공시설(물) 4
 
7.1%
통신주/가로등주 4
 
7.1%
관공서/군부대 4
 
7.1%
학교/종교/의료 4
 
7.1%
요식/유흥/숙박 4
 
7.1%
유통/판매 4
 
7.1%
Other values (4) 16
28.6%

Length

2023-12-12T15:58:19.855993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
발·변전소 4
 
7.1%
송배전선로 4
 
7.1%
공장/작업장 4
 
7.1%
공사장 4
 
7.1%
공공시설(물 4
 
7.1%
통신주/가로등주 4
 
7.1%
관공서/군부대 4
 
7.1%
학교/종교/의료 4
 
7.1%
요식/유흥/숙박 4
 
7.1%
유통/판매 4
 
7.1%
Other values (4) 16
28.6%

인명피해(사망)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4464286
Minimum0
Maximum9
Zeros26
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T15:58:20.287422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.25
95-th percentile5.25
Maximum9
Range9
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.953269
Coefficient of variation (CV)1.3504082
Kurtosis3.2297746
Mean1.4464286
Median Absolute Deviation (MAD)1
Skewness1.7078043
Sum81
Variance3.8152597
MonotonicityNot monotonic
2023-12-12T15:58:20.452349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 26
46.4%
1 11
19.6%
3 6
 
10.7%
2 5
 
8.9%
4 4
 
7.1%
6 2
 
3.6%
9 1
 
1.8%
5 1
 
1.8%
ValueCountFrequency (%)
0 26
46.4%
1 11
19.6%
2 5
 
8.9%
3 6
 
10.7%
4 4
 
7.1%
5 1
 
1.8%
6 2
 
3.6%
9 1
 
1.8%
ValueCountFrequency (%)
9 1
 
1.8%
6 2
 
3.6%
5 1
 
1.8%
4 4
 
7.1%
3 6
 
10.7%
2 5
 
8.9%
1 11
19.6%
0 26
46.4%

인명피해(부상)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.464286
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T15:58:20.603526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q16
median17.5
Q336
95-th percentile113.75
Maximum174
Range173
Interquartile range (IQR)30

Descriptive statistics

Standard deviation39.83738
Coefficient of variation (CV)1.2661142
Kurtosis4.4637669
Mean31.464286
Median Absolute Deviation (MAD)12
Skewness2.1354736
Sum1762
Variance1587.0169
MonotonicityNot monotonic
2023-12-12T15:58:20.754855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
6 5
 
8.9%
3 4
 
7.1%
8 4
 
7.1%
1 3
 
5.4%
36 2
 
3.6%
5 2
 
3.6%
39 2
 
3.6%
10 2
 
3.6%
28 2
 
3.6%
15 1
 
1.8%
Other values (29) 29
51.8%
ValueCountFrequency (%)
1 3
5.4%
2 1
 
1.8%
3 4
7.1%
4 1
 
1.8%
5 2
 
3.6%
6 5
8.9%
7 1
 
1.8%
8 4
7.1%
10 2
 
3.6%
11 1
 
1.8%
ValueCountFrequency (%)
174 1
1.8%
170 1
1.8%
119 1
1.8%
112 1
1.8%
103 1
1.8%
96 1
1.8%
80 1
1.8%
77 1
1.8%
57 1
1.8%
53 1
1.8%

Interactions

2023-12-12T15:58:19.209070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:19.057388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:19.298298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:19.124009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:58:20.867577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도장소인명피해(사망)인명피해(부상)
연도1.0000.0000.0000.000
장소0.0001.0000.6030.841
인명피해(사망)0.0000.6031.0000.803
인명피해(부상)0.0000.8410.8031.000
2023-12-12T15:58:20.979984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장소연도
장소1.0000.000
연도0.0001.000
2023-12-12T15:58:21.091198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인명피해(사망)인명피해(부상)연도장소
인명피해(사망)1.0000.5100.0000.290
인명피해(부상)0.5101.0000.0000.538
연도0.0000.0001.0000.000
장소0.2900.5380.0001.000

Missing values

2023-12-12T15:58:19.406715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:58:19.484257image/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

연도장소인명피해(사망)인명피해(부상)
02018발·변전소11
12018송배전선로150
22018공장/작업장3170
32018공사장153
42018공공시설(물)26
52018통신주/가로등주03
62018관공서/군부대08
72018학교/종교/의료014
82018요식/유흥/숙박015
92018유통/판매23
연도장소인명피해(사망)인명피해(부상)
462021공공시설(물)01
472021통신주/가로등주08
482021관공서/군부대05
492021학교/종교/의료38
502021요식/유흥/숙박05
512021유통/판매03
522021빌딩/오피스텔057
532021주거시설477
542021야외/해상628
552021기타433