Overview

Dataset statistics

Number of variables8
Number of observations65
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory71.0 B

Variable types

Categorical3
Numeric5

Dataset

Description- 도로종류별(일반국도, 고속국도 등), 도로형태별(터널안, 교량위 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/15070255/fileData.do

Alerts

도로형태_대분류 is highly overall correlated with 도로형태High correlation
도로형태 is highly overall correlated with 도로형태_대분류High 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 8 (12.3%) zerosZeros
중상자수 has 1 (1.5%) zerosZeros
경상자수 has 1 (1.5%) zerosZeros
부상신고자수 has 5 (7.7%) zerosZeros

Reproduction

Analysis started2023-12-12 05:45:55.512682
Analysis finished2023-12-12 05:45:59.164500
Duration3.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

Distinct7
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size652.0 B
특별광역시도
10 
시도
10 
기타
10 
일반국도
9 
지방도
9 
Other values (2)
17 

Length

Max length6
Median length4
Mean length3.2769231
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
특별광역시도 10
15.4%
시도 10
15.4%
기타 10
15.4%
일반국도 9
13.8%
지방도 9
13.8%
군도 9
13.8%
고속국도 8
12.3%

Length

2023-12-12T14:45:59.242088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:45:59.383948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특별광역시도 10
15.4%
시도 10
15.4%
기타 10
15.4%
일반국도 9
13.8%
지방도 9
13.8%
군도 9
13.8%
고속국도 8
12.3%

도로형태_대분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size652.0 B
단일로
35 
교차로
20 
기타/불명
7 
철길건널목
 
3

Length

Max length5
Median length3
Mean length3.3076923
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단일로 35
53.8%
교차로 20
30.8%
기타/불명 7
 
10.8%
철길건널목 3
 
4.6%

Length

2023-12-12T14:45:59.536850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:45:59.694367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단일로 35
53.8%
교차로 20
30.8%
기타/불명 7
 
10.8%
철길건널목 3
 
4.6%

도로형태
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size652.0 B
터널안
7 
교량위
7 
고가도로위
7 
지하차도(도로)내
7 
기타단일로
7 
Other values (5)
30 

Length

Max length9
Median length8
Mean length5.1692308
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row터널안
2nd row교량위
3rd row고가도로위
4th row지하차도(도로)내
5th row기타단일로

Common Values

ValueCountFrequency (%)
터널안 7
10.8%
교량위 7
10.8%
고가도로위 7
10.8%
지하차도(도로)내 7
10.8%
기타단일로 7
10.8%
교차로내 7
10.8%
교차로부근 7
10.8%
기타/불명 7
10.8%
교차로횡단보도내 6
9.2%
철길건널목 3
4.6%

Length

2023-12-12T14:45:59.828367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:46:00.019960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
터널안 7
10.8%
교량위 7
10.8%
고가도로위 7
10.8%
지하차도(도로)내 7
10.8%
기타단일로 7
10.8%
교차로내 7
10.8%
교차로부근 7
10.8%
기타/불명 7
10.8%
교차로횡단보도내 6
9.2%
철길건널목 3
4.6%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3028.2462
Minimum1
Maximum34288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T14:46:00.527974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.6
Q179
median409
Q32810
95-th percentile18078.2
Maximum34288
Range34287
Interquartile range (IQR)2731

Descriptive statistics

Standard deviation6257.1039
Coefficient of variation (CV)2.0662468
Kurtosis11.493592
Mean3028.2462
Median Absolute Deviation (MAD)399
Skewness3.2540546
Sum196836
Variance39151349
MonotonicityNot monotonic
2023-12-12T14:46:00.673179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 2
 
3.1%
1 2
 
3.1%
12 2
 
3.1%
24 2
 
3.1%
10 2
 
3.1%
890 1
 
1.5%
21433 1
 
1.5%
2810 1
 
1.5%
10796 1
 
1.5%
2 1
 
1.5%
Other values (50) 50
76.9%
ValueCountFrequency (%)
1 2
3.1%
2 1
1.5%
7 1
1.5%
10 2
3.1%
12 2
3.1%
24 2
3.1%
25 1
1.5%
31 1
1.5%
34 1
1.5%
46 1
1.5%
ValueCountFrequency (%)
34288 1
1.5%
22709 1
1.5%
21433 1
1.5%
19379 1
1.5%
12875 1
1.5%
10796 1
1.5%
8169 1
1.5%
5763 1
1.5%
5265 1
1.5%
5052 1
1.5%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.076923
Minimum0
Maximum373
Zeros8
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T14:46:00.831478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q334
95-th percentile197
Maximum373
Range373
Interquartile range (IQR)31

Descriptive statistics

Standard deviation76.08472
Coefficient of variation (CV)1.8082292
Kurtosis7.7033016
Mean42.076923
Median Absolute Deviation (MAD)9
Skewness2.7478487
Sum2735
Variance5788.8846
MonotonicityNot monotonic
2023-12-12T14:46:01.012508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 8
 
12.3%
9 6
 
9.2%
1 5
 
7.7%
3 3
 
4.6%
6 3
 
4.6%
18 3
 
4.6%
2 3
 
4.6%
5 2
 
3.1%
71 2
 
3.1%
7 2
 
3.1%
Other values (27) 28
43.1%
ValueCountFrequency (%)
0 8
12.3%
1 5
7.7%
2 3
 
4.6%
3 3
 
4.6%
5 2
 
3.1%
6 3
 
4.6%
7 2
 
3.1%
8 1
 
1.5%
9 6
9.2%
10 1
 
1.5%
ValueCountFrequency (%)
373 1
1.5%
294 1
1.5%
290 1
1.5%
200 1
1.5%
185 1
1.5%
152 1
1.5%
131 1
1.5%
126 1
1.5%
93 1
1.5%
74 1
1.5%

중상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean795.61538
Minimum0
Maximum7603
Zeros1
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T14:46:01.197676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q129
median109
Q3991
95-th percentile4726.6
Maximum7603
Range7603
Interquartile range (IQR)962

Descriptive statistics

Standard deviation1549.9532
Coefficient of variation (CV)1.9481187
Kurtosis8.8500223
Mean795.61538
Median Absolute Deviation (MAD)107
Skewness2.9605417
Sum51715
Variance2402354.8
MonotonicityNot monotonic
2023-12-12T14:46:01.363376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
6.2%
35 2
 
3.1%
20 2
 
3.1%
8 2
 
3.1%
109 2
 
3.1%
9 2
 
3.1%
42 1
 
1.5%
95 1
 
1.5%
6103 1
 
1.5%
1114 1
 
1.5%
Other values (47) 47
72.3%
ValueCountFrequency (%)
0 1
 
1.5%
1 4
6.2%
2 1
 
1.5%
4 1
 
1.5%
6 1
 
1.5%
7 1
 
1.5%
8 2
3.1%
9 2
3.1%
13 1
 
1.5%
20 2
3.1%
ValueCountFrequency (%)
7603 1
1.5%
6103 1
1.5%
5878 1
1.5%
5229 1
1.5%
2717 1
1.5%
2383 1
1.5%
2358 1
1.5%
1743 1
1.5%
1726 1
1.5%
1315 1
1.5%

경상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3268.1538
Minimum0
Maximum36082
Zeros1
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T14:46:01.508806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.4
Q177
median477
Q32924
95-th percentile19718.2
Maximum36082
Range36082
Interquartile range (IQR)2847

Descriptive statistics

Standard deviation6714.5774
Coefficient of variation (CV)2.0545475
Kurtosis10.552267
Mean3268.1538
Median Absolute Deviation (MAD)454
Skewness3.1380231
Sum212430
Variance45085549
MonotonicityNot monotonic
2023-12-12T14:46:01.682376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
3.1%
183 1
 
1.5%
477 1
 
1.5%
12788 1
 
1.5%
0 1
 
1.5%
2708 1
 
1.5%
11 1
 
1.5%
30 1
 
1.5%
22 1
 
1.5%
259 1
 
1.5%
Other values (54) 54
83.1%
ValueCountFrequency (%)
0 1
1.5%
1 2
3.1%
11 1
1.5%
13 1
1.5%
19 1
1.5%
22 1
1.5%
23 1
1.5%
30 1
1.5%
31 1
1.5%
32 1
1.5%
ValueCountFrequency (%)
36082 1
1.5%
23741 1
1.5%
23228 1
1.5%
20965 1
1.5%
14731 1
1.5%
12788 1
1.5%
9369 1
1.5%
6711 1
1.5%
6026 1
1.5%
5465 1
1.5%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.66154
Minimum0
Maximum3056
Zeros5
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T14:46:01.841467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median32
Q3249
95-th percentile1509
Maximum3056
Range3056
Interquartile range (IQR)244

Descriptive statistics

Standard deviation547.48072
Coefficient of variation (CV)2.0153045
Kurtosis11.830421
Mean271.66154
Median Absolute Deviation (MAD)32
Skewness3.2305368
Sum17658
Variance299735.13
MonotonicityNot monotonic
2023-12-12T14:46:02.003640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 6
 
9.2%
0 5
 
7.7%
2 4
 
6.2%
5 3
 
4.6%
18 3
 
4.6%
16 1
 
1.5%
256 1
 
1.5%
52 1
 
1.5%
2037 1
 
1.5%
1716 1
 
1.5%
Other values (39) 39
60.0%
ValueCountFrequency (%)
0 5
7.7%
1 6
9.2%
2 4
6.2%
3 1
 
1.5%
5 3
4.6%
7 1
 
1.5%
9 1
 
1.5%
11 1
 
1.5%
16 1
 
1.5%
17 1
 
1.5%
ValueCountFrequency (%)
3056 1
1.5%
2037 1
1.5%
1716 1
1.5%
1644 1
1.5%
969 1
1.5%
856 1
1.5%
854 1
1.5%
660 1
1.5%
623 1
1.5%
553 1
1.5%

Interactions

2023-12-12T14:45:58.416660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:55.873990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.583546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.200700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.791349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.533835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:55.998000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.693714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.325856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.905832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.629153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.150048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.829231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.447645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.015399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.736068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.342066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.971036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.547109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.150555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.855318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:56.463774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.085625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:57.685289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:58.259073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:46:02.127876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류도로형태_대분류도로형태사고건수사망자수중상자수경상자수부상신고자수
도로종류1.0000.0000.0000.2280.0000.0000.1680.000
도로형태_대분류0.0001.0001.0000.0000.0960.0000.0000.000
도로형태0.0001.0001.0000.3950.4910.3450.2570.477
사고건수0.2280.0000.3951.0000.8780.9471.0000.983
사망자수0.0000.0960.4910.8781.0000.9770.9690.879
중상자수0.0000.0000.3450.9470.9771.0000.9840.929
경상자수0.1680.0000.2571.0000.9690.9841.0000.946
부상신고자수0.0000.0000.4770.9830.8790.9290.9461.000
2023-12-12T14:46:02.266450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류도로형태_대분류도로형태
도로종류1.0000.0000.000
도로형태_대분류0.0001.0000.950
도로형태0.0000.9501.000
2023-12-12T14:46:02.375286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고건수사망자수중상자수경상자수부상신고자수도로종류도로형태_대분류도로형태
사고건수1.0000.9440.9890.9900.9770.0710.0000.201
사망자수0.9441.0000.9540.9320.9330.0000.0000.251
중상자수0.9890.9541.0000.9770.9690.0000.0000.162
경상자수0.9900.9320.9771.0000.9840.0780.0000.113
부상신고자수0.9770.9330.9690.9841.0000.0000.0000.254
도로종류0.0710.0000.0000.0780.0001.0000.0000.000
도로형태_대분류0.0000.0000.0000.0000.0000.0001.0000.950
도로형태0.2010.2510.1620.1130.2540.0000.9501.000

Missing values

2023-12-12T14:45:59.003776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:45:59.122655image/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

도로종류도로형태_대분류도로형태사고건수사망자수중상자수경상자수부상신고자수
0일반국도단일로터널안11634218316
1일반국도단일로교량위14496516518
2일반국도단일로고가도로위1086351415
3일반국도단일로지하차도(도로)내4171815848131
4일반국도단일로기타단일로816929423839369856
5일반국도교차로교차로내57637417266711553
6일반국도교차로교차로횡단보도내577922535533
7일반국도교차로교차로부근3795348514741391
8일반국도기타/불명기타/불명1499213491672118
9지방도단일로터널안3127481
도로종류도로형태_대분류도로형태사고건수사망자수중상자수경상자수부상신고자수
55기타단일로터널안3408552
56기타단일로교량위56120632
57기타단일로고가도로위2409230
58기타단일로지하차도(도로)내62113815
59기타단일로기타단일로40607110353763431
60기타교차로교차로내2742157362924249
61기타교차로교차로횡단보도내27478718818
62기타교차로교차로부근147092851568144
63기타철길건널목철길건널목10010
64기타기타/불명기타/불명4081299323916417