Overview

Dataset statistics

Number of variables11
Number of observations210
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.4 KiB
Average record size in memory94.6 B

Variable types

Categorical4
Text2
Numeric5

Dataset

Description17년 부터 20년도 까지의 중대재해 발생이 규모별 동종업종 평균재해율 이상인 사업장 - 연도, 지역, 업종명, 규모, 사업장명(현장명), 소재지, 중대재해자수(명), 근로자수(명), 재해자수(명), 재해율(%), 규모별 동종 업종 평균 재해율(%) - 율 표기 : 재해율(%),규모별 동종 업종 평균 재해율(%)
URLhttps://www.data.go.kr/data/15090150/fileData.do

Alerts

중대재해재해자수(명) is highly overall correlated with 재해자수(명)High correlation
근로자수(명) is highly overall correlated with 재해율(퍼센트) and 1 other fieldsHigh correlation
재해자수(명) is highly overall correlated with 중대재해재해자수(명) and 1 other fieldsHigh correlation
재해율(퍼센트) is highly overall correlated with 근로자수(명)High correlation
규모별 동종업종 평균 재해율(퍼센트) is highly overall correlated with 업종명(중분류)High correlation
연도 is highly overall correlated with 지역 High correlation
지역 is highly overall correlated with 연도 High correlation
업종명(중분류) is highly overall correlated with 규모별 동종업종 평균 재해율(퍼센트)High correlation
규모 is highly overall correlated with 근로자수(명) and 1 other fieldsHigh correlation
규모 is highly imbalanced (64.3%)Imbalance

Reproduction

Analysis started2023-12-12 09:21:05.947393
Analysis finished2023-12-12 09:21:10.210563
Duration4.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2020
147 
2019
53 
2018
 
6
2017
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2018

Common Values

ValueCountFrequency (%)
2020 147
70.0%
2019 53
 
25.2%
2018 6
 
2.9%
2017 4
 
1.9%

Length

2023-12-12T18:21:10.280022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:10.422390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 147
70.0%
2019 53
 
25.2%
2018 6
 
2.9%
2017 4
 
1.9%

지역
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
경기
49 
서울
25 
충남
22 
경북
15 
부산
15 
Other values (17)
84 

Length

Max length4
Median length2
Mean length2.0619048
Min length2

Unique

Unique3 ?
Unique (%)1.4%

Sample

1st row대구
2nd row충북
3rd row충북
4th row경북
5th row인천

Common Values

ValueCountFrequency (%)
경기 49
23.3%
서울 25
11.9%
충남 22
10.5%
경북 15
 
7.1%
부산 15
 
7.1%
인천 14
 
6.7%
경남 13
 
6.2%
울산 9
 
4.3%
전북 9
 
4.3%
강원 9
 
4.3%
Other values (12) 30
14.3%

Length

2023-12-12T18:21:10.559551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 51
24.3%
서울 25
11.9%
충남 22
10.5%
경북 16
 
7.6%
부산 16
 
7.6%
인천 14
 
6.7%
경남 13
 
6.2%
울산 11
 
5.2%
전북 9
 
4.3%
강원 9
 
4.3%
Other values (6) 24
11.4%

업종명(중분류)
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
건설업
134 
기계기구·금속·비금속광물제품제조업
20 
시설관리및사업지원서비스업
 
7
화학및고무제품제조업
 
5
기계기구,비금속광물및금속제품제조업
 
5
Other values (23)
39 

Length

Max length22
Median length3
Mean length6.7809524
Min length2

Unique

Unique12 ?
Unique (%)5.7%

Sample

1st row섬유또는섬유제품제조업(을)
2nd row건설업
3rd row건설업
4th row비금속광물제품및금속제품제조업또는금속가공업
5th row건설업

Common Values

ValueCountFrequency (%)
건설업 134
63.8%
기계기구·금속·비금속광물제품제조업 20
 
9.5%
시설관리및사업지원서비스업 7
 
3.3%
화학및고무제품제조업 5
 
2.4%
기계기구,비금속광물및금속제품제조업 5
 
2.4%
기타의각종사업 5
 
2.4%
수제품및기타제품제조업 4
 
1.9%
석회석·금속·비금속광업및기타광업 2
 
1.0%
도·소매및소비자용품수리업 2
 
1.0%
선박건조및수리업 2
 
1.0%
Other values (18) 24
 
11.4%

Length

2023-12-12T18:21:10.753232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건설업 136
64.8%
기계기구·금속·비금속광물제품제조업 20
 
9.5%
시설관리및사업지원서비스업 7
 
3.3%
화학및고무제품제조업 5
 
2.4%
기계기구,비금속광물및금속제품제조업 5
 
2.4%
기타의각종사업 5
 
2.4%
수제품및기타제품제조업 4
 
1.9%
어업 2
 
1.0%
섬유또는섬유제품제조업(을 2
 
1.0%
농업 2
 
1.0%
Other values (15) 22
 
10.5%

규모
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
50인미만
174 
50인~99인
 
15
100~299인
 
12
500~999인
 
4
300~499인
 
3
Other values (2)
 
2

Length

Max length9
Median length5
Mean length5.447619
Min length5

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row50인~99인
2nd row100~299인
3rd row50인미만
4th row50인미만
5th row50인~99인

Common Values

ValueCountFrequency (%)
50인미만 174
82.9%
50인~99인 15
 
7.1%
100~299인 12
 
5.7%
500~999인 4
 
1.9%
300~499인 3
 
1.4%
1,000인이상 1
 
0.5%
100인~299인 1
 
0.5%

Length

2023-12-12T18:21:10.940549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:11.116593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50인미만 174
82.9%
50인~99인 15
 
7.1%
100~299인 12
 
5.7%
500~999인 4
 
1.9%
300~499인 3
 
1.4%
1,000인이상 1
 
0.5%
100인~299인 1
 
0.5%

사업장명(현장명)
Text

Distinct209
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T18:21:11.391041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length112
Median length51
Mean length29.785714
Min length4

Characters and Unicode

Total characters6255
Distinct characters426
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)99.0%

Sample

1st row원진염직
2nd row일진건설㈜, 삼영글로벌㈜(구)(원청), (주)한성에스엘씨(하청)(청풍호 그린 케이블카 조성공사)
3rd row한국전력공사(원청), 삼우전력합자회사(하청)(154kv국사-봉명T/L지장철탑이설공사)
4th row세화금속공업㈜
5th row(주)신일(원청), ㈜세정철강(하청)(원창동 물류창고 신축공사)
ValueCountFrequency (%)
신축공사 29
 
4.2%
개인사업자 18
 
2.6%
및 17
 
2.4%
공사 10
 
1.4%
설치공사 7
 
1.0%
공장 4
 
0.6%
주상복합 4
 
0.6%
근린생활시설 4
 
0.6%
균열보수 4
 
0.6%
재도장 3
 
0.4%
Other values (569) 594
85.6%
2023-12-12T18:21:11.849284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 526
 
8.4%
) 526
 
8.4%
485
 
7.8%
주 205
 
3.3%
청 192
 
3.1%
사 188
 
3.0%
공 174
 
2.8%
설 128
 
2.0%
원 121
 
1.9%
건 111
 
1.8%
Other values (416) 3599
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4199
67.1%
Open Punctuation 529
 
8.5%
Close Punctuation 529
 
8.5%
Space Separator 485
 
7.8%
Decimal Number 199
 
3.2%
Other Punctuation 118
 
1.9%
Uppercase Letter 81
 
1.3%
Other Symbol 71
 
1.1%
Dash Punctuation 25
 
0.4%
Lowercase Letter 15
 
0.2%
Other values (2) 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 205
 
4.9%
청 192
 
4.6%
사 188
 
4.5%
공 174
 
4.1%
설 128
 
3.0%
원 121
 
2.9%
건 111
 
2.6%
하 105
 
2.5%
이 78
 
1.9%
업 74
 
1.8%
Other values (365) 2823
67.2%
Uppercase Letter
ValueCountFrequency (%)
C 10
12.3%
E 8
9.9%
T 7
8.6%
A 6
 
7.4%
P 6
 
7.4%
L 6
 
7.4%
G 6
 
7.4%
N 6
 
7.4%
S 5
 
6.2%
B 4
 
4.9%
Other values (8) 17
21.0%
Lowercase Letter
ValueCountFrequency (%)
n 3
20.0%
t 2
13.3%
k 2
13.3%
l 1
 
6.7%
w 1
 
6.7%
x 1
 
6.7%
r 1
 
6.7%
o 1
 
6.7%
v 1
 
6.7%
a 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 45
22.6%
2 38
19.1%
0 26
13.1%
4 20
10.1%
3 18
 
9.0%
9 15
 
7.5%
8 13
 
6.5%
6 10
 
5.0%
5 8
 
4.0%
7 6
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 110
93.2%
/ 6
 
5.1%
. 2
 
1.7%
Open Punctuation
ValueCountFrequency (%)
( 526
99.4%
[ 3
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 526
99.4%
] 3
 
0.6%
Space Separator
ValueCountFrequency (%)
485
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 71
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4270
68.3%
Common 1889
30.2%
Latin 96
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 205
 
4.8%
청 192
 
4.5%
사 188
 
4.4%
공 174
 
4.1%
설 128
 
3.0%
원 121
 
2.8%
건 111
 
2.6%
하 105
 
2.5%
이 78
 
1.8%
업 74
 
1.7%
Other values (366) 2894
67.8%
Latin
ValueCountFrequency (%)
C 10
 
10.4%
E 8
 
8.3%
T 7
 
7.3%
A 6
 
6.2%
P 6
 
6.2%
L 6
 
6.2%
G 6
 
6.2%
N 6
 
6.2%
S 5
 
5.2%
B 4
 
4.2%
Other values (19) 32
33.3%
Common
ValueCountFrequency (%)
( 526
27.8%
) 526
27.8%
485
25.7%
, 110
 
5.8%
1 45
 
2.4%
2 38
 
2.0%
0 26
 
1.4%
- 25
 
1.3%
4 20
 
1.1%
3 18
 
1.0%
Other values (11) 70
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4169
66.7%
ASCII 1985
31.7%
None 71
 
1.1%
Compat Jamo 30
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 526
26.5%
) 526
26.5%
485
24.4%
, 110
 
5.5%
1 45
 
2.3%
2 38
 
1.9%
0 26
 
1.3%
- 25
 
1.3%
4 20
 
1.0%
3 18
 
0.9%
Other values (40) 166
 
8.4%
Hangul
ValueCountFrequency (%)
주 205
 
4.9%
청 192
 
4.6%
사 188
 
4.5%
공 174
 
4.2%
설 128
 
3.1%
원 121
 
2.9%
건 111
 
2.7%
하 105
 
2.5%
이 78
 
1.9%
업 74
 
1.8%
Other values (364) 2793
67.0%
None
ValueCountFrequency (%)
㈜ 71
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 30
100.0%

사업장 소재지
Text

Distinct209
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T18:21:12.219438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length43
Mean length25.309524
Min length12

Characters and Unicode

Total characters5315
Distinct characters333
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)99.0%

Sample

1st row대구 서구 달서천로 146 (평리동)
2nd row충북 제천시 청풍면 물태리 산6-29(임) 외 15필지
3rd row충북 청주시 흥덕구 외북로 65 (외북동)
4th row경북 고령군 다산면 다산산단로 20
5th row인천 서구 북항로120번길 95 (원창동)
ValueCountFrequency (%)
경기 50
 
4.2%
서울 24
 
2.0%
충남 22
 
1.9%
경북 15
 
1.3%
부산 15
 
1.3%
경남 13
 
1.1%
인천 12
 
1.0%
울산 11
 
0.9%
전북 9
 
0.8%
강원 8
 
0.7%
Other values (791) 1008
84.9%
2023-12-12T18:21:12.807981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1018
 
19.2%
1 157
 
3.0%
동 146
 
2.7%
로 142
 
2.7%
시 120
 
2.3%
산 117
 
2.2%
2 115
 
2.2%
) 113
 
2.1%
( 113
 
2.1%
3 112
 
2.1%
Other values (323) 3162
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3032
57.0%
Space Separator 1018
 
19.2%
Decimal Number 882
 
16.6%
Close Punctuation 113
 
2.1%
Open Punctuation 113
 
2.1%
Dash Punctuation 86
 
1.6%
Other Punctuation 45
 
0.8%
Uppercase Letter 20
 
0.4%
Lowercase Letter 5
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 146
 
4.8%
로 142
 
4.7%
시 120
 
4.0%
산 117
 
3.9%
구 103
 
3.4%
경 90
 
3.0%
남 81
 
2.7%
길 75
 
2.5%
서 73
 
2.4%
기 59
 
1.9%
Other values (290) 2026
66.8%
Uppercase Letter
ValueCountFrequency (%)
P 3
15.0%
K 2
10.0%
C 2
10.0%
T 2
10.0%
B 2
10.0%
L 2
10.0%
S 2
10.0%
E 2
10.0%
Y 1
 
5.0%
M 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 157
17.8%
2 115
13.0%
3 112
12.7%
5 85
9.6%
0 78
8.8%
4 74
8.4%
8 72
8.2%
6 68
7.7%
7 62
 
7.0%
9 59
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
t 1
20.0%
n 1
20.0%
a 1
20.0%
l 1
20.0%
m 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 42
93.3%
. 3
 
6.7%
Space Separator
ValueCountFrequency (%)
1018
100.0%
Close Punctuation
ValueCountFrequency (%)
) 113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3032
57.0%
Common 2258
42.5%
Latin 25
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 146
 
4.8%
로 142
 
4.7%
시 120
 
4.0%
산 117
 
3.9%
구 103
 
3.4%
경 90
 
3.0%
남 81
 
2.7%
길 75
 
2.5%
서 73
 
2.4%
기 59
 
1.9%
Other values (290) 2026
66.8%
Common
ValueCountFrequency (%)
1018
45.1%
1 157
 
7.0%
2 115
 
5.1%
) 113
 
5.0%
( 113
 
5.0%
3 112
 
5.0%
- 86
 
3.8%
5 85
 
3.8%
0 78
 
3.5%
4 74
 
3.3%
Other values (7) 307
 
13.6%
Latin
ValueCountFrequency (%)
P 3
12.0%
K 2
 
8.0%
C 2
 
8.0%
T 2
 
8.0%
B 2
 
8.0%
L 2
 
8.0%
S 2
 
8.0%
E 2
 
8.0%
t 1
 
4.0%
n 1
 
4.0%
Other values (6) 6
24.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3032
57.0%
ASCII 2283
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1018
44.6%
1 157
 
6.9%
2 115
 
5.0%
) 113
 
4.9%
( 113
 
4.9%
3 112
 
4.9%
- 86
 
3.8%
5 85
 
3.7%
0 78
 
3.4%
4 74
 
3.2%
Other values (23) 332
 
14.5%
Hangul
ValueCountFrequency (%)
동 146
 
4.8%
로 142
 
4.7%
시 120
 
4.0%
산 117
 
3.9%
구 103
 
3.4%
경 90
 
3.0%
남 81
 
2.7%
길 75
 
2.5%
서 73
 
2.4%
기 59
 
1.9%
Other values (290) 2026
66.8%

중대재해재해자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3619048
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T18:21:12.963698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum15
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3133383
Coefficient of variation (CV)0.96433934
Kurtosis66.970985
Mean1.3619048
Median Absolute Deviation (MAD)0
Skewness7.5332182
Sum286
Variance1.7248576
MonotonicityNot monotonic
2023-12-12T18:21:13.108340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 166
79.0%
2 38
 
18.1%
3 2
 
1.0%
5 1
 
0.5%
8 1
 
0.5%
10 1
 
0.5%
15 1
 
0.5%
ValueCountFrequency (%)
1 166
79.0%
2 38
 
18.1%
3 2
 
1.0%
5 1
 
0.5%
8 1
 
0.5%
10 1
 
0.5%
15 1
 
0.5%
ValueCountFrequency (%)
15 1
 
0.5%
10 1
 
0.5%
8 1
 
0.5%
5 1
 
0.5%
3 2
 
1.0%
2 38
 
18.1%
1 166
79.0%

근로자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.804762
Minimum1
Maximum4245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T18:21:13.304685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q327.25
95-th percentile265.05
Maximum4245
Range4244
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation306.54827
Coefficient of variation (CV)4.9599458
Kurtosis167.88891
Mean61.804762
Median Absolute Deviation (MAD)8
Skewness12.388709
Sum12979
Variance93971.842
MonotonicityNot monotonic
2023-12-12T18:21:13.465840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 49
23.3%
2 17
 
8.1%
4 11
 
5.2%
3 9
 
4.3%
5 7
 
3.3%
11 7
 
3.3%
24 6
 
2.9%
13 6
 
2.9%
16 5
 
2.4%
8 5
 
2.4%
Other values (56) 88
41.9%
ValueCountFrequency (%)
1 49
23.3%
2 17
 
8.1%
3 9
 
4.3%
4 11
 
5.2%
5 7
 
3.3%
6 3
 
1.4%
7 2
 
1.0%
8 5
 
2.4%
9 3
 
1.4%
10 5
 
2.4%
ValueCountFrequency (%)
4245 1
0.5%
622 1
0.5%
584 1
0.5%
538 1
0.5%
505 1
0.5%
498 1
0.5%
399 1
0.5%
333 1
0.5%
289 2
1.0%
270 1
0.5%

재해자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0904762
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T18:21:13.586561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum38
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.312497
Coefficient of variation (CV)1.5845657
Kurtosis72.158619
Mean2.0904762
Median Absolute Deviation (MAD)0
Skewness7.6676394
Sum439
Variance10.972636
MonotonicityNot monotonic
2023-12-12T18:21:13.723318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 122
58.1%
2 55
26.2%
3 13
 
6.2%
4 7
 
3.3%
5 6
 
2.9%
6 2
 
1.0%
38 1
 
0.5%
19 1
 
0.5%
15 1
 
0.5%
8 1
 
0.5%
ValueCountFrequency (%)
1 122
58.1%
2 55
26.2%
3 13
 
6.2%
4 7
 
3.3%
5 6
 
2.9%
6 2
 
1.0%
8 1
 
0.5%
15 1
 
0.5%
18 1
 
0.5%
19 1
 
0.5%
ValueCountFrequency (%)
38 1
 
0.5%
19 1
 
0.5%
18 1
 
0.5%
15 1
 
0.5%
8 1
 
0.5%
6 2
 
1.0%
5 6
 
2.9%
4 7
 
3.3%
3 13
 
6.2%
2 55
26.2%

재해율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.71281
Minimum0.34
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T18:21:13.880231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile1.195
Q16.06
median18.02
Q3100
95-th percentile155
Maximum300
Range299.66
Interquartile range (IQR)93.94

Descriptive statistics

Standard deviation54.106445
Coefficient of variation (CV)1.2377709
Kurtosis3.2463332
Mean43.71281
Median Absolute Deviation (MAD)15.235
Skewness1.7384775
Sum9179.69
Variance2927.5074
MonotonicityNot monotonic
2023-12-12T18:21:14.053233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 44
21.0%
50.0 14
 
6.7%
200.0 10
 
4.8%
25.0 9
 
4.3%
20.0 8
 
3.8%
33.33 7
 
3.3%
12.5 6
 
2.9%
8.33 5
 
2.4%
6.25 5
 
2.4%
7.69 4
 
1.9%
Other values (72) 98
46.7%
ValueCountFrequency (%)
0.34 1
0.5%
0.35 1
0.5%
0.6 2
1.0%
0.8 1
0.5%
1.04 2
1.0%
1.09 1
0.5%
1.11 1
0.5%
1.12 1
0.5%
1.15 1
0.5%
1.25 1
0.5%
ValueCountFrequency (%)
300.0 1
 
0.5%
200.0 10
 
4.8%
100.0 44
21.0%
66.67 4
 
1.9%
60.0 1
 
0.5%
50.0 14
 
6.7%
33.33 7
 
3.3%
30.77 1
 
0.5%
30.0 1
 
0.5%
25.0 9
 
4.3%

규모별 동종업종 평균 재해율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3767143
Minimum0.19
Maximum3.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T18:21:14.566639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.32
Q10.74
median1.82
Q31.82
95-th percentile2
Maximum3.14
Range2.95
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.61492612
Coefficient of variation (CV)0.44666212
Kurtosis-0.81721595
Mean1.3767143
Median Absolute Deviation (MAD)0.18
Skewness-0.30467754
Sum289.11
Variance0.37813413
MonotonicityNot monotonic
2023-12-12T18:21:14.705490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1.82 80
38.1%
2.0 23
 
11.0%
1.12 19
 
9.0%
0.74 9
 
4.3%
0.58 8
 
3.8%
0.48 7
 
3.3%
1.8 5
 
2.4%
1.39 5
 
2.4%
0.32 4
 
1.9%
0.55 3
 
1.4%
Other values (32) 47
22.4%
ValueCountFrequency (%)
0.19 1
 
0.5%
0.2 3
1.4%
0.25 1
 
0.5%
0.27 1
 
0.5%
0.31 2
1.0%
0.32 4
1.9%
0.39 1
 
0.5%
0.44 1
 
0.5%
0.46 3
1.4%
0.47 1
 
0.5%
ValueCountFrequency (%)
3.14 2
 
1.0%
2.32 1
 
0.5%
2.19 1
 
0.5%
2.0 23
 
11.0%
1.82 80
38.1%
1.8 5
 
2.4%
1.78 1
 
0.5%
1.45 1
 
0.5%
1.39 5
 
2.4%
1.36 2
 
1.0%

Interactions

2023-12-12T18:21:09.302283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:06.925735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.528704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.104702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.689892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:09.419497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.053298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.647433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.211010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.845008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:09.511038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.169230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.735768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.331183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.946263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:09.641480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.284760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.866123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.442327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:09.074625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:09.767757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.400732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:07.979104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:08.558008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:09.189529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:21:14.812156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역업종명(중분류)규모중대재해재해자수(명)근로자수(명)재해자수(명)재해율(퍼센트)규모별 동종업종 평균 재해율(퍼센트)
연도1.0000.8760.6650.0000.5050.0000.2680.0000.597
지역0.8761.0000.6060.0000.5820.0000.4290.0000.281
업종명(중분류)0.6650.6061.0000.0000.0000.0000.0000.4020.954
규모0.0000.0000.0001.0000.4390.9430.7460.0000.533
중대재해재해자수(명)0.5050.5820.0000.4391.0000.5600.8210.0000.000
근로자수(명)0.0000.0000.0000.9430.5601.0000.7570.0000.525
재해자수(명)0.2680.4290.0000.7460.8210.7571.0000.0000.321
재해율(퍼센트)0.0000.0000.4020.0000.0000.0000.0001.0000.138
규모별 동종업종 평균 재해율(퍼센트)0.5970.2810.9540.5330.0000.5250.3210.1381.000
2023-12-12T18:21:14.934284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역규모연도업종명(중분류)
지역1.0000.0000.6530.191
규모0.0001.0000.0000.000
연도0.6530.0001.0000.353
업종명(중분류)0.1910.0000.3531.000
2023-12-12T18:21:15.042538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중대재해재해자수(명)근로자수(명)재해자수(명)재해율(퍼센트)규모별 동종업종 평균 재해율(퍼센트)연도지역업종명(중분류)규모
중대재해재해자수(명)1.0000.1250.5840.054-0.1370.3470.2990.0000.279
근로자수(명)0.1251.0000.473-0.955-0.4170.0000.0000.0000.955
재해자수(명)0.5840.4731.000-0.223-0.2840.2200.2170.0000.603
재해율(퍼센트)0.054-0.955-0.2231.0000.3920.0000.0000.1790.000
규모별 동종업종 평균 재해율(퍼센트)-0.137-0.417-0.2840.3921.0000.4190.0370.7330.313
연도0.3470.0000.2200.0000.4191.0000.6530.3530.000
지역0.2990.0000.2170.0000.0370.6531.0000.1910.000
업종명(중분류)0.0000.0000.0000.1790.7330.3530.1911.0000.000
규모0.2790.9550.6030.0000.3130.0000.0000.0001.000

Missing values

2023-12-12T18:21:09.945771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:21:10.134028image/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

연도지역업종명(중분류)규모사업장명(현장명)사업장 소재지중대재해재해자수(명)근로자수(명)재해자수(명)재해율(퍼센트)규모별 동종업종 평균 재해율(퍼센트)
02017대구섬유또는섬유제품제조업(을)50인~99인원진염직대구 서구 달서천로 146 (평리동)17711.30.69
12017충북건설업100~299인일진건설㈜, 삼영글로벌㈜(구)(원청), (주)한성에스엘씨(하청)(청풍호 그린 케이블카 조성공사)충북 제천시 청풍면 물태리 산6-29(임) 외 15필지514653.420.31
22017충북건설업50인미만한국전력공사(원청), 삼우전력합자회사(하청)(154kv국사-봉명T/L지장철탑이설공사)충북 청주시 흥덕구 외북로 65 (외북동)11516.671.78
32017경북비금속광물제품및금속제품제조업또는금속가공업50인미만세화금속공업㈜경북 고령군 다산면 다산산단로 2024524.441.8
42018인천건설업50인~99인(주)신일(원청), ㈜세정철강(하청)(원창동 물류창고 신축공사)인천 서구 북항로120번길 95 (원창동)17911.270.72
52018인천건설업50인미만관악개발㈜(영흥화력제 2부두 하자보수공사)인천 옹진군 영흥면 영흥남로293번길 75 영흥화력발전본부 전면해상222100.01.8
62018세종건설업100~299인(주)부원건설(원청), 유아건설(하청)(세종 TREESHADE 주상복합아파트 신축공사)세종 새롬동 2203-181943819.590.47
72018경기건설업50인미만욕실나라(성남보미리즌빌상가 철거공사)경기 성남시 수정구 위례광장로 97 (창곡동, 위례 자연앤 센트럴자이 보미리즌빌상가101)111100.01.8
82018경기건설업50인미만흥안이앤씨㈜(원청), (주)유스틸(하청)((주)가연 본사사옥 신축공사)경기 안산시 단원구 산단로 295 (원시동)13313.031.8
92018경북섬유또는섬유제품제조업(을)50인미만(주)한영(원청), 세인ENG(하청)경북 고령군 대가야읍 장기공단1길 38-1123326.061.01
연도지역업종명(중분류)규모사업장명(현장명)사업장 소재지중대재해재해자수(명)근로자수(명)재해자수(명)재해율(퍼센트)규모별 동종업종 평균 재해율(퍼센트)
2002020경남기계기구·금속·비금속광물제품제조업50인미만(주)서광이엔지경남 함안군 칠원읍 원서로 139-34 (칠원읍)11417.141.12
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