Overview

Dataset statistics

Number of variables6
Number of observations1300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.1 KiB
Average record size in memory52.1 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 총사망자수(명), 안전사고 사망자수(명), 안전사고 사망률(퍼센트)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110161

Alerts

총사망자수(명) is highly overall correlated with 안전사고 사망자수(명) and 1 other fieldsHigh correlation
안전사고 사망자수(명) is highly overall correlated with 총사망자수(명)High correlation
안전사고 사망률(퍼센트) is highly overall correlated with 총사망자수(명)High correlation

Reproduction

Analysis started2023-12-11 00:02:29.567050
Analysis finished2023-12-11 00:02:31.153043
Duration1.59 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2016
260 
2017
260 
2018
260 
2019
260 
2020
260 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 260
20.0%
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%

Length

2023-12-11T09:02:31.220546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:31.329761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 260
20.0%
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
경기도
240 
서울특별시
125 
경상북도
125 
경상남도
115 
전라남도
110 
Other values (11)
585 

Length

Max length7
Median length5
Mean length4.0538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 240
18.5%
서울특별시 125
9.6%
경상북도 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
부산광역시 80
 
6.2%
전라북도 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

Length

2023-12-11T09:02:31.462434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 240
18.5%
서울특별시 125
9.6%
경상북도 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
부산광역시 80
 
6.2%
전라북도 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

시군구명
Text

Distinct236
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T09:02:31.820021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9623077
Min length2

Characters and Unicode

Total characters3851
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 30
 
2.3%
동구 30
 
2.3%
남구 27
 
2.1%
북구 25
 
1.9%
서구 25
 
1.9%
강서구 10
 
0.8%
고성군 10
 
0.8%
남원시 5
 
0.4%
덕진구 5
 
0.4%
군산시 5
 
0.4%
Other values (226) 1128
86.8%
2023-12-11T09:02:32.279944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.0%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1816
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3851
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.0%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1816
47.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3851
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.0%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1816
47.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3851
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.0%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1816
47.2%

총사망자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct987
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1283.8631
Minimum63
Maximum5627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T09:02:32.420074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile328.8
Q1647.75
median1134
Q31659
95-th percentile2760.35
Maximum5627
Range5564
Interquartile range (IQR)1011.25

Descriptive statistics

Standard deviation861.50198
Coefficient of variation (CV)0.67102325
Kurtosis4.1087583
Mean1283.8631
Median Absolute Deviation (MAD)500.5
Skewness1.663569
Sum1669022
Variance742185.66
MonotonicityNot monotonic
2023-12-11T09:02:32.543975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
994 5
 
0.4%
710 4
 
0.3%
1214 4
 
0.3%
1110 4
 
0.3%
1145 4
 
0.3%
897 4
 
0.3%
669 4
 
0.3%
1094 4
 
0.3%
1144 4
 
0.3%
1192 4
 
0.3%
Other values (977) 1259
96.8%
ValueCountFrequency (%)
63 1
0.1%
64 1
0.1%
67 1
0.1%
70 1
0.1%
76 1
0.1%
146 1
0.1%
167 1
0.1%
173 1
0.1%
174 1
0.1%
175 1
0.1%
ValueCountFrequency (%)
5627 1
0.1%
5615 1
0.1%
5473 1
0.1%
5362 1
0.1%
5294 1
0.1%
5119 1
0.1%
4993 1
0.1%
4939 1
0.1%
4913 1
0.1%
4857 1
0.1%

안전사고 사망자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.356154
Minimum0
Maximum170
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T09:02:32.683502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q120
median32
Q347
95-th percentile81
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation23.057616
Coefficient of variation (CV)0.63421494
Kurtosis4.4306855
Mean36.356154
Median Absolute Deviation (MAD)13
Skewness1.6580653
Sum47263
Variance531.65366
MonotonicityNot monotonic
2023-12-11T09:02:32.812917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 41
 
3.2%
17 36
 
2.8%
21 36
 
2.8%
31 32
 
2.5%
37 32
 
2.5%
11 32
 
2.5%
20 30
 
2.3%
33 29
 
2.2%
26 29
 
2.2%
27 29
 
2.2%
Other values (105) 974
74.9%
ValueCountFrequency (%)
0 1
 
0.1%
1 1
 
0.1%
2 3
 
0.2%
3 1
 
0.1%
4 5
 
0.4%
5 4
 
0.3%
6 8
0.6%
7 11
0.8%
8 9
0.7%
9 15
1.2%
ValueCountFrequency (%)
170 1
0.1%
156 1
0.1%
155 1
0.1%
154 1
0.1%
153 1
0.1%
147 1
0.1%
144 1
0.1%
139 1
0.1%
133 2
0.2%
127 1
0.1%

안전사고 사망률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct1105
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.642408
Minimum0
Maximum100.11
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T09:02:32.958424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.768
Q112.4075
median18.165
Q334.455
95-th percentile58.6025
Maximum100.11
Range100.11
Interquartile range (IQR)22.0475

Descriptive statistics

Standard deviation16.21239
Coefficient of variation (CV)0.6579061
Kurtosis0.76967037
Mean24.642408
Median Absolute Deviation (MAD)7.735
Skewness1.1578204
Sum32035.13
Variance262.8416
MonotonicityNot monotonic
2023-12-11T09:02:33.097574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.9 5
 
0.4%
12.79 5
 
0.4%
16.85 4
 
0.3%
11.59 4
 
0.3%
9.72 4
 
0.3%
11.96 4
 
0.3%
15.29 3
 
0.2%
13.56 3
 
0.2%
12.56 3
 
0.2%
17.97 3
 
0.2%
Other values (1095) 1262
97.1%
ValueCountFrequency (%)
0.0 1
0.1%
2.83 1
0.1%
3.54 1
0.1%
4.33 1
0.1%
4.37 1
0.1%
4.6 1
0.1%
4.63 1
0.1%
4.7 1
0.1%
4.75 2
0.2%
4.77 1
0.1%
ValueCountFrequency (%)
100.11 1
0.1%
88.03 1
0.1%
82.19 1
0.1%
80.21 1
0.1%
77.93 1
0.1%
77.92 1
0.1%
77.14 1
0.1%
75.51 1
0.1%
75.3 1
0.1%
74.62 1
0.1%

Interactions

2023-12-11T09:02:30.633868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:29.938471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.299855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.731490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.054911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.407763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.832117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.181668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:30.514978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:02:33.194542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명총사망자수(명)안전사고 사망자수(명)안전사고 사망률(퍼센트)
통계연도1.0000.0000.0000.0000.100
시도명0.0001.0000.5580.4170.550
총사망자수(명)0.0000.5581.0000.9070.593
안전사고 사망자수(명)0.0000.4170.9071.0000.422
안전사고 사망률(퍼센트)0.1000.5500.5930.4221.000
2023-12-11T09:02:33.287314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T09:02:33.360627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총사망자수(명)안전사고 사망자수(명)안전사고 사망률(퍼센트)통계연도시도명
총사망자수(명)1.0000.878-0.6160.0000.256
안전사고 사망자수(명)0.8781.000-0.2810.0000.171
안전사고 사망률(퍼센트)-0.616-0.2811.0000.0420.251
통계연도0.0000.0000.0421.0000.000
시도명0.2560.1710.2510.0001.000

Missing values

2023-12-11T09:02:30.969998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:02:31.097512image/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

통계연도시도명시군구명총사망자수(명)안전사고 사망자수(명)안전사고 사망률(퍼센트)
02016서울특별시종로구8232516.85
12016서울특별시중구6691915.68
22016서울특별시용산구11373415.32
32016서울특별시성동구1304299.9
42016서울특별시광진구1344308.49
52016서울특별시동대문구19214713.4
62016서울특별시중랑구20136014.76
72016서울특별시성북구21395712.75
82016서울특별시강북구18455416.71
92016서울특별시도봉구1819318.97
통계연도시도명시군구명총사망자수(명)안전사고 사망자수(명)안전사고 사망률(퍼센트)
12902020경상남도창녕군7992337.5
12912020경상남도고성군6402038.89
12922020경상남도남해군7101637.21
12932020경상남도하동군6242759.49
12942020경상남도산청군5211234.44
12952020경상남도함양군5581846.1
12962020경상남도거창군7133048.8
12972020경상남도합천군7351943.03
12982020제주특별자치도제주시27158417.25
12992020제주특별자치도서귀포시12374927.16