Overview

Dataset statistics

Number of variables7
Number of observations78
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory62.7 B

Variable types

Categorical2
Numeric5

Dataset

Description- 도로종류별(일반국도, 고속국도 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/15070228/fileData.do

Alerts

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 10 (12.8%) zerosZeros

Reproduction

Analysis started2023-12-12 19:58:36.680308
Analysis finished2023-12-12 19:58:40.151697
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

Distinct7
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size756.0 B
일반국도
12 
지방도
12 
특별광역시도
12 
시도
12 
군도
12 
Other values (2)
18 

Length

Max length6
Median length4
Mean length3.2307692
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반국도 12
15.4%
지방도 12
15.4%
특별광역시도 12
15.4%
시도 12
15.4%
군도 12
15.4%
기타 12
15.4%
고속국도 6
7.7%

Length

2023-12-13T04:58:40.255678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:58:40.417669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반국도 12
15.4%
지방도 12
15.4%
특별광역시도 12
15.4%
시도 12
15.4%
군도 12
15.4%
기타 12
15.4%
고속국도 6
7.7%

가해자차종
Categorical

Distinct12
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size756.0 B
승용차
7 
승합차
7 
화물차
7 
특수차
7 
건설기계
7 
Other values (7)
43 

Length

Max length11
Median length3
Mean length4.8846154
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
승용차 7
9.0%
승합차 7
9.0%
화물차 7
9.0%
특수차 7
9.0%
건설기계 7
9.0%
기타/불명 7
9.0%
이륜차 6
7.7%
사륜오토바이(ATV) 6
7.7%
원동기장치자전거 6
7.7%
자전거 6
7.7%
Other values (2) 12
15.4%

Length

2023-12-13T04:58:40.587102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용차 7
9.0%
승합차 7
9.0%
화물차 7
9.0%
특수차 7
9.0%
건설기계 7
9.0%
기타/불명 7
9.0%
이륜차 6
7.7%
사륜오토바이(atv 6
7.7%
원동기장치자전거 6
7.7%
자전거 6
7.7%
Other values (2) 12
15.4%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2523.5385
Minimum6
Maximum51545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T04:58:41.081877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile24.85
Q1108.5
median334
Q31385.75
95-th percentile7841.8
Maximum51545
Range51539
Interquartile range (IQR)1277.25

Descriptive statistics

Standard deviation7642.6694
Coefficient of variation (CV)3.0285528
Kurtosis30.551769
Mean2523.5385
Median Absolute Deviation (MAD)294
Skewness5.3694888
Sum196836
Variance58410395
MonotonicityNot monotonic
2023-12-13T04:58:41.287308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 2
 
2.6%
187 2
 
2.6%
24 2
 
2.6%
151 2
 
2.6%
113 2
 
2.6%
25 1
 
1.3%
83 1
 
1.3%
31 1
 
1.3%
162 1
 
1.3%
116 1
 
1.3%
Other values (63) 63
80.8%
ValueCountFrequency (%)
6 1
1.3%
23 1
1.3%
24 2
2.6%
25 1
1.3%
31 1
1.3%
34 1
1.3%
37 1
1.3%
43 1
1.3%
45 1
1.3%
57 1
1.3%
ValueCountFrequency (%)
51545 1
1.3%
41840 1
1.3%
13299 1
1.3%
7988 1
1.3%
7816 1
1.3%
7486 1
1.3%
7336 1
1.3%
7170 1
1.3%
4743 1
1.3%
4524 1
1.3%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.064103
Minimum0
Maximum369
Zeros10
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T04:58:41.470864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9.5
Q329.75
95-th percentile141.25
Maximum369
Range369
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation64.135882
Coefficient of variation (CV)1.8291038
Kurtosis12.193645
Mean35.064103
Median Absolute Deviation (MAD)8.5
Skewness3.2423408
Sum2735
Variance4113.4114
MonotonicityNot monotonic
2023-12-13T04:58:41.648321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 10
 
12.8%
1 5
 
6.4%
6 4
 
5.1%
7 4
 
5.1%
9 4
 
5.1%
3 4
 
5.1%
5 3
 
3.8%
4 3
 
3.8%
96 2
 
2.6%
23 2
 
2.6%
Other values (30) 37
47.4%
ValueCountFrequency (%)
0 10
12.8%
1 5
6.4%
2 2
 
2.6%
3 4
 
5.1%
4 3
 
3.8%
5 3
 
3.8%
6 4
 
5.1%
7 4
 
5.1%
9 4
 
5.1%
10 2
 
2.6%
ValueCountFrequency (%)
369 1
1.3%
290 1
1.3%
224 1
1.3%
154 1
1.3%
139 1
1.3%
124 1
1.3%
116 1
1.3%
102 1
1.3%
96 2
2.6%
77 1
1.3%

중상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean663.01282
Minimum1
Maximum11401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T04:58:41.814697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.55
Q129.25
median100.5
Q3437.75
95-th percentile2227.8
Maximum11401
Range11400
Interquartile range (IQR)408.5

Descriptive statistics

Standard deviation1808.3437
Coefficient of variation (CV)2.7274642
Kurtosis26.737336
Mean663.01282
Median Absolute Deviation (MAD)86
Skewness4.9972408
Sum51715
Variance3270107.1
MonotonicityNot monotonic
2023-12-13T04:58:41.998280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 3
 
3.8%
6 2
 
2.6%
71 2
 
2.6%
49 2
 
2.6%
25 2
 
2.6%
27 2
 
2.6%
242 1
 
1.3%
40 1
 
1.3%
37 1
 
1.3%
56 1
 
1.3%
Other values (61) 61
78.2%
ValueCountFrequency (%)
1 1
 
1.3%
6 2
2.6%
8 1
 
1.3%
11 1
 
1.3%
14 3
3.8%
15 1
 
1.3%
18 1
 
1.3%
19 1
 
1.3%
21 1
 
1.3%
22 1
 
1.3%
ValueCountFrequency (%)
11401 1
1.3%
10496 1
1.3%
3540 1
1.3%
2374 1
1.3%
2202 1
1.3%
2038 1
1.3%
1923 1
1.3%
1707 1
1.3%
1563 1
1.3%
1492 1
1.3%

경상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2723.4615
Minimum4
Maximum58985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T04:58:42.160956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile17.85
Q178.25
median340.5
Q31243.5
95-th percentile8591
Maximum58985
Range58981
Interquartile range (IQR)1165.25

Descriptive statistics

Standard deviation8705.0063
Coefficient of variation (CV)3.1963023
Kurtosis30.97062
Mean2723.4615
Median Absolute Deviation (MAD)307.5
Skewness5.4144454
Sum212430
Variance75777134
MonotonicityNot monotonic
2023-12-13T04:58:42.315444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 2
 
2.6%
16314 1
 
1.3%
12 1
 
1.3%
86 1
 
1.3%
60 1
 
1.3%
37 1
 
1.3%
371 1
 
1.3%
22 1
 
1.3%
1256 1
 
1.3%
270 1
 
1.3%
Other values (67) 67
85.9%
ValueCountFrequency (%)
4 1
1.3%
11 1
1.3%
12 1
1.3%
17 1
1.3%
18 2
2.6%
22 1
1.3%
23 1
1.3%
26 1
1.3%
29 1
1.3%
37 1
1.3%
ValueCountFrequency (%)
58985 1
1.3%
47114 1
1.3%
16314 1
1.3%
9203 1
1.3%
8483 1
1.3%
7808 1
1.3%
7704 1
1.3%
6198 1
1.3%
5107 1
1.3%
4500 1
1.3%

부상신고자수
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.38462
Minimum1
Maximum3323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-13T04:58:42.487732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median49.5
Q3175
95-th percentile892.75
Maximum3323
Range3322
Interquartile range (IQR)163

Descriptive statistics

Standard deviation531.18222
Coefficient of variation (CV)2.3463707
Kurtosis22.749964
Mean226.38462
Median Absolute Deviation (MAD)45.5
Skewness4.5369076
Sum17658
Variance282154.55
MonotonicityNot monotonic
2023-12-13T04:58:42.668499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5
 
6.4%
1 3
 
3.8%
4 3
 
3.8%
26 2
 
2.6%
12 2
 
2.6%
19 2
 
2.6%
11 2
 
2.6%
29 2
 
2.6%
21 2
 
2.6%
90 1
 
1.3%
Other values (54) 54
69.2%
ValueCountFrequency (%)
1 3
3.8%
2 5
6.4%
4 3
3.8%
5 1
 
1.3%
6 1
 
1.3%
7 1
 
1.3%
8 1
 
1.3%
9 1
 
1.3%
10 1
 
1.3%
11 2
 
2.6%
ValueCountFrequency (%)
3323 1
1.3%
2923 1
1.3%
1189 1
1.3%
1135 1
1.3%
850 1
1.3%
659 1
1.3%
647 1
1.3%
578 1
1.3%
533 1
1.3%
420 1
1.3%

Interactions

2023-12-13T04:58:39.169530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:36.914196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.327337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.879740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.521157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:39.279670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:36.983994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.419681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.984087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.658840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:39.410481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.065957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.532972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.107808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.810363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:39.537966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.145751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.637702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.238506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.912362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:39.696355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.238360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:37.753091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:38.380287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:58:39.039652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:58:42.779447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류가해자차종사고건수사망자수중상자수경상자수부상신고자수
도로종류1.0000.0000.0000.0000.0000.0000.000
가해자차종0.0001.0000.2600.3950.4280.2600.422
사고건수0.0000.2601.0000.9480.9811.0000.910
사망자수0.0000.3950.9481.0000.9020.9480.909
중상자수0.0000.4280.9810.9021.0000.9810.912
경상자수0.0000.2601.0000.9480.9811.0000.910
부상신고자수0.0000.4220.9100.9090.9120.9101.000
2023-12-13T04:58:42.902804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가해자차종도로종류
가해자차종1.0000.000
도로종류0.0001.000
2023-12-13T04:58:43.039697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고건수사망자수중상자수경상자수부상신고자수도로종류가해자차종
사고건수1.0000.8060.9810.9820.9530.0000.132
사망자수0.8061.0000.8670.7920.7220.0000.168
중상자수0.9810.8671.0000.9650.9220.0000.236
경상자수0.9820.7920.9651.0000.9200.0000.132
부상신고자수0.9530.7220.9220.9201.0000.0000.165
도로종류0.0000.0000.0000.0000.0001.0000.000
가해자차종0.1320.1680.2360.1320.1650.0001.000

Missing values

2023-12-13T04:58:39.939839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:58:40.099646image/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

도로종류가해자차종사고건수사망자수중상자수경상자수부상신고자수
0일반국도승용차132992243540163141135
1일반국도승합차1104143571301130
2일반국도화물차327611610663770268
3일반국도특수차18775721819
4일반국도이륜차1438704381206231
5일반국도사륜오토바이(ATV)24314112
6일반국도원동기장치자전거15154911029
7일반국도자전거3451110817885
8일반국도개인형이동장치(PM)18734812242
9일반국도건설기계3591212441521
도로종류가해자차종사고건수사망자수중상자수경상자수부상신고자수
68기타화물차162125471155096
69기타특수차43018411
70기타이륜차93223254734139
71기타사륜오토바이(ATV)34114182
72기타원동기장치자전거16557111518
73기타자전거7329239435143
74기타개인형이동장치(PM)37029326165
75기타건설기계15144115512
76기타농기계581730174
77기타기타/불명29314717586