Overview

Dataset statistics

Number of variables14
Number of observations22
Missing cells157
Missing cells (%)51.0%
Duplicate rows6
Duplicate rows (%)27.3%
Total size in memory2.6 KiB
Average record size in memory119.0 B

Variable types

Unsupported13
Categorical1

Dataset

Description* 음주운전 및 무면허 관련 교통사고 현황(2015~2017년)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094190/fileData.do

Alerts

Dataset has 6 (27.3%) duplicate rowsDuplicates
Unnamed: 0 has 22 (100.0%) missing valuesMissing
Unnamed: 2 has 9 (40.9%) missing valuesMissing
Unnamed: 3 has 12 (54.5%) missing valuesMissing
Unnamed: 4 has 12 (54.5%) missing valuesMissing
Unnamed: 5 has 12 (54.5%) missing valuesMissing
Unnamed: 6 has 9 (40.9%) missing valuesMissing
Unnamed: 7 has 12 (54.5%) missing valuesMissing
Unnamed: 8 has 12 (54.5%) missing valuesMissing
Unnamed: 9 has 12 (54.5%) missing valuesMissing
Unnamed: 10 has 9 (40.9%) missing valuesMissing
Unnamed: 11 has 12 (54.5%) missing valuesMissing
Unnamed: 12 has 12 (54.5%) missing valuesMissing
Unnamed: 13 has 12 (54.5%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 23:20:34.250872
Analysis finished2023-12-12 23:20:34.915894
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22
Missing (%)100.0%
Memory size330.0 B

- 음주운전 사고이면서 무면허 사고가 아닌 교통사고의 사상자 현황
Categorical

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Memory size308.0 B
<NA>
4 
발생년도
3 
사상자성별
3 
남
3 
여
3 
Other values (4)
6 

Length

Max length36
Median length32
Mean length8.0909091
Min length1

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st row발생년도
2nd row사상자성별
3rd row남
4th row여
5th row기타불명

Common Values

ValueCountFrequency (%)
<NA> 4
18.2%
발생년도 3
13.6%
사상자성별 3
13.6%
남 3
13.6%
여 3
13.6%
*사상자현황 통계에서 가해자는 제외 3
13.6%
기타불명 1
 
4.5%
- 무면허 사고이면서 음주운전 사고가 아닌 교통사고의 사상자 현황 1
 
4.5%
- 무면허 사고이고 음주운전 사고인 교통사고의 사상자 현황 1
 
4.5%

Length

2023-12-13T08:20:34.998487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:20:35.119143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4
 
8.7%
사상자성별 3
 
6.5%
남 3
 
6.5%
여 3
 
6.5%
사상자현황 3
 
6.5%
통계에서 3
 
6.5%
가해자는 3
 
6.5%
제외 3
 
6.5%
발생년도 3
 
6.5%
음주운전 2
 
4.3%
Other values (11) 16
34.8%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9
Missing (%)40.9%
Memory size308.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9
Missing (%)40.9%
Memory size308.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9
Missing (%)40.9%
Memory size308.0 B

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12
Missing (%)54.5%
Memory size308.0 B

Missing values

2023-12-13T08:20:34.357318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:20:34.553100image/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.
2023-12-13T08:20:34.744400image/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

Unnamed: 0- 음주운전 사고이면서 무면허 사고가 아닌 교통사고의 사상자 현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
0<NA>발생년도2015NaNNaNNaN2016NaNNaNNaN2017NaNNaNNaN
1<NA>사상자성별사망자수중상자수경상자수부상신고자수사망자수중상자수경상자수부상신고자수사망자수중상자수경상자수부상신고자수
2<NA>남195508718043771152390815114587150370914474574
3<NA>여952907959038173216578202706120657511285
4<NA>기타불명000000001000
5<NA>*사상자현황 통계에서 가해자는 제외NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6<NA><NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7<NA><NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8<NA>- 무면허 사고이면서 음주운전 사고가 아닌 교통사고의 사상자 현황NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9<NA>발생년도2015NaNNaNNaN2016NaNNaNNaN2017NaNNaNNaN
Unnamed: 0- 음주운전 사고이면서 무면허 사고가 아닌 교통사고의 사상자 현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
12<NA>여2253513661171033410195510428119351
13<NA>*사상자현황 통계에서 가해자는 제외NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14<NA><NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
15<NA><NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
16<NA>- 무면허 사고이고 음주운전 사고인 교통사고의 사상자 현황NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
17<NA>발생년도2015NaNNaNNaN2016NaNNaNNaN2017NaNNaNNaN
18<NA>사상자성별사망자수중상자수경상자수부상신고자수사망자수중상자수경상자수부상신고자수사망자수중상자수경상자수부상신고자수
19<NA>남142821142629150592411021084835
20<NA>여7181649312843287210548215
21<NA>*사상자현황 통계에서 가해자는 제외NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

- 음주운전 사고이면서 무면허 사고가 아닌 교통사고의 사상자 현황# duplicates
5<NA>4
0*사상자현황 통계에서 가해자는 제외3
1남3
2발생년도3
3사상자성별3
4여3