Overview

Dataset statistics

Number of variables3
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory25.6 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description국내에 등록되어 도로를 운행 중인 자동차의 시군구별 자동차 주행거리 당 사망자수에 대한 현황입니다. 단위(명/10억km)
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15088445/fileData.do

Reproduction

Analysis started2023-12-12 17:40:39.150737
Analysis finished2023-12-12 17:40:39.457725
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
31 
서울특별시
25 
경상북도
23 
전라남도
22 
강원도
18 
Other values (12)
110 

Length

Max length7
Median length5
Mean length4.1484716
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전라북도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 36
15.7%

Length

2023-12-13T02:40:39.531067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전라북도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 36
15.7%

시군구
Text

Distinct207
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T02:40:39.820240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0611354
Min length3

Characters and Unicode

Total characters701
Distinct characters138
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)87.3%

Sample

1st row종로구
2nd row중 구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
구 25
 
9.8%
중 6
 
2.4%
동 6
 
2.4%
서 5
 
2.0%
남 4
 
1.6%
북 4
 
1.6%
고성군 2
 
0.8%
강서구 2
 
0.8%
담양군 1
 
0.4%
무주군 1
 
0.4%
Other values (198) 198
78.0%
2023-12-13T02:40:40.294675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
12.1%
시 79
 
11.3%
구 74
 
10.6%
25
 
3.6%
천 22
 
3.1%
주 20
 
2.9%
성 18
 
2.6%
양 18
 
2.6%
동 17
 
2.4%
산 16
 
2.3%
Other values (128) 327
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 676
96.4%
Space Separator 25
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 676
96.4%
Common 25
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 676
96.4%
ASCII 25
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%
ASCII
ValueCountFrequency (%)
25
100.0%

주행거리당 사망자수
Real number (ℝ)

Distinct165
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.286463
Minimum0
Maximum83.9
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T02:40:40.484833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.54
Q16.4
median11.9
Q323
95-th percentile41.1
Maximum83.9
Range83.9
Interquartile range (IQR)16.6

Descriptive statistics

Standard deviation13.272268
Coefficient of variation (CV)0.81492638
Kurtosis3.0696822
Mean16.286463
Median Absolute Deviation (MAD)6.3
Skewness1.5520925
Sum3729.6
Variance176.15311
MonotonicityNot monotonic
2023-12-13T02:40:40.649267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 5
 
2.2%
5.6 4
 
1.7%
7.5 4
 
1.7%
6.3 4
 
1.7%
4.3 4
 
1.7%
7.0 3
 
1.3%
5.7 3
 
1.3%
33.7 3
 
1.3%
4.5 3
 
1.3%
5.0 3
 
1.3%
Other values (155) 193
84.3%
ValueCountFrequency (%)
0.0 1
0.4%
1.3 1
0.4%
1.7 1
0.4%
2.0 2
0.9%
2.3 1
0.4%
2.4 1
0.4%
2.9 1
0.4%
3.0 1
0.4%
3.2 1
0.4%
3.3 1
0.4%
ValueCountFrequency (%)
83.9 1
0.4%
62.7 1
0.4%
59.3 1
0.4%
51.4 1
0.4%
47.8 1
0.4%
47.7 1
0.4%
47.1 1
0.4%
47.0 1
0.4%
45.2 1
0.4%
45.1 1
0.4%

Interactions

2023-12-13T02:40:39.280038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:40:40.753894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도주행거리당 사망자수
시도1.0000.514
주행거리당 사망자수0.5141.000
2023-12-13T02:40:40.854335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주행거리당 사망자수시도
주행거리당 사망자수1.0000.228
시도0.2281.000

Missing values

2023-12-13T02:40:39.372560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:40:39.433939image/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

시도시군구주행거리당 사망자수
0서울특별시종로구10.4
1서울특별시중 구7.2
2서울특별시용산구12.0
3서울특별시성동구8.9
4서울특별시광진구2.4
5서울특별시동대문구12.3
6서울특별시중랑구7.9
7서울특별시성북구5.5
8서울특별시강북구6.1
9서울특별시도봉구3.2
시도시군구주행거리당 사망자수
219경상남도창녕군14.6
220경상남도고성군27.0
221경상남도남해군29.4
222경상남도하동군32.9
223경상남도산청군6.7
224경상남도함양군26.6
225경상남도거창군35.1
226경상남도합천군31.3
227제주특별자치도제주시5.1
228제주특별자치도서귀포시21.2