Overview

Dataset statistics

Number of variables9
Number of observations195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory77.7 B

Variable types

Categorical4
Numeric5

Dataset

Description- 사고유형별(차대사람, 차대차 등), 가해운전자 차종별(승용차, 승합차 등) 교통사고 통계 - 경찰에서 조사, 처리한 교통사고에 대한 통계 정보로 인적 피해가 있는 사고만 집계 됨 - 교통사고분석시스템(http://taas.koroad.or.kr)의 데이터를 바탕으로 함
URLhttps://www.data.go.kr/data/15070281/fileData.do

Alerts

사고유형대분류 is highly overall correlated with 사고유형중분류 and 1 other fieldsHigh correlation
사고유형 is highly overall correlated with 사고유형대분류 and 1 other fieldsHigh correlation
사고유형중분류 is highly overall correlated with 사고유형대분류 and 1 other fieldsHigh correlation
사고건수 is highly overall correlated with 사망자수 and 3 other fieldsHigh correlation
사망자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
중상자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
경상자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
부상신고자수 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
사망자수 has 55 (28.2%) zerosZeros
중상자수 has 14 (7.2%) zerosZeros
경상자수 has 9 (4.6%) zerosZeros
부상신고자수 has 41 (21.0%) zerosZeros

Reproduction

Analysis started2023-12-12 21:45:09.735610
Analysis finished2023-12-12 21:45:13.529551
Duration3.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사고유형대분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
차량단독
76 
차대차
60 
차대사람
57 
철길건널목
 
2

Length

Max length5
Median length4
Mean length3.7025641
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
차량단독 76
39.0%
차대차 60
30.8%
차대사람 57
29.2%
철길건널목 2
 
1.0%

Length

2023-12-13T06:45:13.638604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:13.795800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량단독 76
39.0%
차대차 60
30.8%
차대사람 57
29.2%
철길건널목 2
 
1.0%

사고유형중분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기타
36 
도로이탈
22 
차도통행중
12 
정면충돌
12 
측면충돌
12 
Other values (10)
101 

Length

Max length10
Median length9
Mean length3.9846154
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 36
18.5%
도로이탈 22
11.3%
차도통행중 12
 
6.2%
정면충돌 12
 
6.2%
측면충돌 12
 
6.2%
후진중충돌 12
 
6.2%
추돌 12
 
6.2%
전도 12
 
6.2%
공작물충돌 12
 
6.2%
횡단중 11
 
5.6%
Other values (5) 42
21.5%

Length

2023-12-13T06:45:13.935067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 36
17.8%
도로이탈 22
10.9%
차도통행중 12
 
5.9%
정면충돌 12
 
5.9%
측면충돌 12
 
5.9%
후진중충돌 12
 
5.9%
추돌 12
 
5.9%
전도 12
 
5.9%
공작물충돌 12
 
5.9%
횡단중 11
 
5.4%
Other values (6) 49
24.3%

사고유형
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기타
36 
차도통행중
12 
정면충돌
12 
측면충돌
12 
후진중충돌
12 
Other values (11)
111 

Length

Max length10
Median length9
Mean length4.3230769
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 36
18.5%
차도통행중 12
 
6.2%
정면충돌 12
 
6.2%
측면충돌 12
 
6.2%
후진중충돌 12
 
6.2%
추돌 12
 
6.2%
전도 12
 
6.2%
공작물충돌 12
 
6.2%
횡단중 11
 
5.6%
길가장자리구역통행중 11
 
5.6%
Other values (6) 53
27.2%

Length

2023-12-13T06:45:14.085288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 47
21.0%
도로이탈 22
 
9.8%
정면충돌 12
 
5.4%
측면충돌 12
 
5.4%
후진중충돌 12
 
5.4%
추돌 12
 
5.4%
전도 12
 
5.4%
공작물충돌 12
 
5.4%
차도통행중 12
 
5.4%
전복 11
 
4.9%
Other values (7) 60
26.8%

가해자차종
Categorical

Distinct12
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
승용차
18 
화물차
18 
승합차
17 
이륜차
17 
원동기장치자전거
17 
Other values (7)
108 

Length

Max length11
Median length3
Mean length4.9435897
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row승용차
2nd row승합차
3rd row화물차
4th row특수차
5th row이륜차

Common Values

ValueCountFrequency (%)
승용차 18
9.2%
화물차 18
9.2%
승합차 17
8.7%
이륜차 17
8.7%
원동기장치자전거 17
8.7%
자전거 17
8.7%
개인형이동장치(PM) 16
8.2%
건설기계 16
8.2%
특수차 15
7.7%
사륜오토바이(ATV) 15
7.7%
Other values (2) 29
14.9%

Length

2023-12-13T06:45:14.252344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용차 18
9.2%
화물차 18
9.2%
승합차 17
8.7%
이륜차 17
8.7%
원동기장치자전거 17
8.7%
자전거 17
8.7%
개인형이동장치(pm 16
8.2%
건설기계 16
8.2%
특수차 15
7.7%
사륜오토바이(atv 15
7.7%
Other values (2) 29
14.9%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009.4154
Minimum1
Maximum44525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:45:14.425979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median66
Q3379
95-th percentile4048
Maximum44525
Range44524
Interquartile range (IQR)369

Descriptive statistics

Standard deviation4255.9883
Coefficient of variation (CV)4.2162903
Kurtosis68.352341
Mean1009.4154
Median Absolute Deviation (MAD)63
Skewness7.7757813
Sum196836
Variance18113437
MonotonicityNot monotonic
2023-12-13T06:45:14.651127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 11
 
5.6%
1 9
 
4.6%
4 6
 
3.1%
21 6
 
3.1%
9 5
 
2.6%
6 5
 
2.6%
3 5
 
2.6%
15 4
 
2.1%
221 4
 
2.1%
5 4
 
2.1%
Other values (116) 136
69.7%
ValueCountFrequency (%)
1 9
4.6%
2 11
5.6%
3 5
2.6%
4 6
3.1%
5 4
 
2.1%
6 5
2.6%
7 3
 
1.5%
9 5
2.6%
10 3
 
1.5%
11 3
 
1.5%
ValueCountFrequency (%)
44525 1
0.5%
29018 1
0.5%
21989 1
0.5%
9021 1
0.5%
8773 1
0.5%
7918 1
0.5%
5285 1
0.5%
5275 1
0.5%
4908 1
0.5%
4706 1
0.5%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.025641
Minimum0
Maximum245
Zeros55
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:45:14.820309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile87.6
Maximum245
Range245
Interquartile range (IQR)10

Descriptive statistics

Standard deviation32.793235
Coefficient of variation (CV)2.3380917
Kurtosis17.609435
Mean14.025641
Median Absolute Deviation (MAD)2
Skewness3.8582318
Sum2735
Variance1075.3962
MonotonicityNot monotonic
2023-12-13T06:45:14.997456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 55
28.2%
1 30
15.4%
2 16
 
8.2%
5 13
 
6.7%
3 11
 
5.6%
4 9
 
4.6%
7 5
 
2.6%
10 5
 
2.6%
12 3
 
1.5%
6 3
 
1.5%
Other values (36) 45
23.1%
ValueCountFrequency (%)
0 55
28.2%
1 30
15.4%
2 16
 
8.2%
3 11
 
5.6%
4 9
 
4.6%
5 13
 
6.7%
6 3
 
1.5%
7 5
 
2.6%
8 1
 
0.5%
9 2
 
1.0%
ValueCountFrequency (%)
245 1
0.5%
156 1
0.5%
151 1
0.5%
143 1
0.5%
137 1
0.5%
121 1
0.5%
100 1
0.5%
98 1
0.5%
91 1
0.5%
89 1
0.5%

중상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.20513
Minimum0
Maximum9749
Zeros14
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:45:15.189721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median17
Q3107
95-th percentile1076.1
Maximum9749
Range9749
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation958.8781
Coefficient of variation (CV)3.6156092
Kurtosis57.437405
Mean265.20513
Median Absolute Deviation (MAD)16
Skewness6.9362767
Sum51715
Variance919447.21
MonotonicityNot monotonic
2023-12-13T06:45:15.329413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 14
 
7.2%
0 14
 
7.2%
3 12
 
6.2%
1 9
 
4.6%
4 7
 
3.6%
6 7
 
3.6%
12 6
 
3.1%
5 6
 
3.1%
24 5
 
2.6%
7 4
 
2.1%
Other values (91) 111
56.9%
ValueCountFrequency (%)
0 14
7.2%
1 9
4.6%
2 14
7.2%
3 12
6.2%
4 7
3.6%
5 6
3.1%
6 7
3.6%
7 4
 
2.1%
8 2
 
1.0%
9 2
 
1.0%
ValueCountFrequency (%)
9749 1
0.5%
6003 1
0.5%
4114 1
0.5%
3826 1
0.5%
2650 1
0.5%
2284 1
0.5%
1979 1
0.5%
1580 1
0.5%
1343 1
0.5%
1095 1
0.5%

경상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct113
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1089.3846
Minimum0
Maximum56044
Zeros9
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:45:15.484735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14.5
median33
Q3280.5
95-th percentile3806.3
Maximum56044
Range56044
Interquartile range (IQR)276

Descriptive statistics

Standard deviation5351.1485
Coefficient of variation (CV)4.9120838
Kurtosis71.044399
Mean1089.3846
Median Absolute Deviation (MAD)32
Skewness8.0605272
Sum212430
Variance28634790
MonotonicityNot monotonic
2023-12-13T06:45:15.654773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
9.2%
2 15
 
7.7%
0 9
 
4.6%
6 9
 
4.6%
9 5
 
2.6%
3 4
 
2.1%
18 4
 
2.1%
16 4
 
2.1%
5 4
 
2.1%
34 3
 
1.5%
Other values (103) 120
61.5%
ValueCountFrequency (%)
0 9
4.6%
1 18
9.2%
2 15
7.7%
3 4
 
2.1%
4 3
 
1.5%
5 4
 
2.1%
6 9
4.6%
7 2
 
1.0%
8 1
 
0.5%
9 5
 
2.6%
ValueCountFrequency (%)
56044 1
0.5%
34766 1
0.5%
32807 1
0.5%
9152 1
0.5%
6979 1
0.5%
6603 1
0.5%
5982 1
0.5%
5699 1
0.5%
5083 1
0.5%
4808 1
0.5%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.553846
Minimum0
Maximum3264
Zeros41
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T06:45:15.835447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q339
95-th percentile367.6
Maximum3264
Range3264
Interquartile range (IQR)38

Descriptive statistics

Standard deviation329.80485
Coefficient of variation (CV)3.6420855
Kurtosis57.580916
Mean90.553846
Median Absolute Deviation (MAD)6
Skewness7.1085532
Sum17658
Variance108771.24
MonotonicityNot monotonic
2023-12-13T06:45:16.012222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
21.0%
1 23
 
11.8%
2 15
 
7.7%
3 9
 
4.6%
6 5
 
2.6%
5 4
 
2.1%
9 4
 
2.1%
24 4
 
2.1%
13 4
 
2.1%
20 3
 
1.5%
Other values (70) 83
42.6%
ValueCountFrequency (%)
0 41
21.0%
1 23
11.8%
2 15
 
7.7%
3 9
 
4.6%
4 1
 
0.5%
5 4
 
2.1%
6 5
 
2.6%
7 1
 
0.5%
9 4
 
2.1%
10 2
 
1.0%
ValueCountFrequency (%)
3264 1
0.5%
2296 1
0.5%
1831 1
0.5%
844 1
0.5%
702 1
0.5%
688 1
0.5%
448 1
0.5%
441 1
0.5%
411 1
0.5%
369 1
0.5%

Interactions

2023-12-13T06:45:12.677366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.199069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.810262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.641168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.177846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.778436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.290580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.892235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.745342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.277683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.874840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.418242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.015661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.856602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.379597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.989912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.545286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.121749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.953877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.475001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:13.107746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:10.686311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:11.528504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.064474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:12.570893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:45:16.131321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고유형대분류사고유형중분류사고유형가해자차종사고건수사망자수중상자수경상자수부상신고자수
사고유형대분류1.0000.9710.9970.0000.1110.0000.0000.0600.195
사고유형중분류0.9711.0001.0000.0000.0000.0000.0000.0000.000
사고유형0.9971.0001.0000.0000.0000.0000.0000.0000.000
가해자차종0.0000.0000.0001.0000.2010.5050.1180.2120.280
사고건수0.1110.0000.0000.2011.0000.8180.9500.9650.993
사망자수0.0000.0000.0000.5050.8181.0000.8590.7670.767
중상자수0.0000.0000.0000.1180.9500.8591.0000.9700.922
경상자수0.0600.0000.0000.2120.9650.7670.9701.0000.955
부상신고자수0.1950.0000.0000.2800.9930.7670.9220.9551.000
2023-12-13T06:45:16.270217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고유형대분류가해자차종사고유형사고유형중분류
사고유형대분류1.0000.0000.8990.902
가해자차종0.0001.0000.0000.000
사고유형0.8990.0001.0000.997
사고유형중분류0.9020.0000.9971.000
2023-12-13T06:45:16.397271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고건수사망자수중상자수경상자수부상신고자수사고유형대분류사고유형중분류사고유형가해자차종
사고건수1.0000.6720.9700.9780.9320.0710.0000.0000.077
사망자수0.6721.0000.7240.5920.6070.0000.0000.0000.249
중상자수0.9700.7241.0000.9340.8930.0000.0000.0000.054
경상자수0.9780.5920.9341.0000.9120.0480.0000.0000.115
부상신고자수0.9320.6070.8930.9121.0000.1250.0000.0000.110
사고유형대분류0.0710.0000.0000.0480.1251.0000.9020.8990.000
사고유형중분류0.0000.0000.0000.0000.0000.9021.0000.9970.000
사고유형0.0000.0000.0000.0000.0000.8990.9971.0000.000
가해자차종0.0770.2490.0540.1150.1100.0000.0000.0001.000

Missing values

2023-12-13T06:45:13.258903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:45:13.441546image/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

사고유형대분류사고유형중분류사고유형가해자차종사고건수사망자수중상자수경상자수부상신고자수
0차대사람횡단중횡단중승용차877324538264808301
1차대사람횡단중횡단중승합차6874334631715
2차대사람횡단중횡단중화물차15299178767135
3차대사람횡단중횡단중특수차35711161
4차대사람횡단중횡단중이륜차13982871779195
5차대사람횡단중횡단중사륜오토바이(ATV)71321
6차대사람횡단중횡단중원동기장치자전거1383558210
7차대사람횡단중횡단중자전거22107414134
8차대사람횡단중횡단중개인형이동장치(PM)15303811613
9차대사람횡단중횡단중건설기계621435150
사고유형대분류사고유형중분류사고유형가해자차종사고건수사망자수중상자수경상자수부상신고자수
185차량단독기타기타이륜차49649165174135
186차량단독기타기타사륜오토바이(ATV)33212157
187차량단독기타기타원동기장치자전거725362018
188차량단독기타기타자전거11718323037
189차량단독기타기타개인형이동장치(PM)1505316949
190차량단독기타기타건설기계62320
191차량단독기타기타농기계33131361
192차량단독기타기타기타/불명150485
193철길건널목철길건널목철길건널목승용차21211
194철길건널목철길건널목철길건널목화물차22010