Overview

Dataset statistics

Number of variables7
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory64.1 B

Variable types

Categorical2
Numeric5

Dataset

Description- 도로종류별(일반국도, 고속국도 등), 기상상태별(맑음, 안개 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/15070237/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
사고건수 has unique valuesUnique
경상자수 has unique valuesUnique
사망자수 has 4 (9.5%) zerosZeros
부상신고자수 has 1 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-12 06:35:35.619389
Analysis finished2023-12-12 06:35:39.085869
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
일반국도
6 
지방도
6 
특별광역시도
6 
시도
6 
군도
6 
Other values (2)
12 

Length

Max length6
Median length4
Mean length3.2857143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반국도 6
14.3%
지방도 6
14.3%
특별광역시도 6
14.3%
시도 6
14.3%
군도 6
14.3%
고속국도 6
14.3%
기타 6
14.3%

Length

2023-12-12T15:35:39.161714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:39.297169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반국도 6
14.3%
지방도 6
14.3%
특별광역시도 6
14.3%
시도 6
14.3%
군도 6
14.3%
고속국도 6
14.3%
기타 6
14.3%

기상상태
Categorical

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size468.0 B
맑음
7 
흐림
7 
비
7 
안개
7 
눈
7 

Length

Max length5
Median length3.5
Mean length2.1666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row맑음
2nd row흐림
3rd row비
4th row안개
5th row눈

Common Values

ValueCountFrequency (%)
맑음 7
16.7%
흐림 7
16.7%
비 7
16.7%
안개 7
16.7%
눈 7
16.7%
기타/불명 7
16.7%

Length

2023-12-12T15:35:39.481879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:39.612079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
맑음 7
16.7%
흐림 7
16.7%
비 7
16.7%
안개 7
16.7%
눈 7
16.7%
기타/불명 7
16.7%

사고건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4686.5714
Minimum4
Maximum70134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T15:35:39.747263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10.05
Q155
median350
Q31732.25
95-th percentile17990.8
Maximum70134
Range70130
Interquartile range (IQR)1677.25

Descriptive statistics

Standard deviation13814.492
Coefficient of variation (CV)2.9476756
Kurtosis16.437423
Mean4686.5714
Median Absolute Deviation (MAD)317.5
Skewness4.0447543
Sum196836
Variance1.9084019 × 108
MonotonicityNot monotonic
2023-12-12T15:35:39.915708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
18348 1
 
2.4%
325 1
 
2.4%
6486 1
 
2.4%
251 1
 
2.4%
274 1
 
2.4%
8 1
 
2.4%
48 1
 
2.4%
16 1
 
2.4%
4382 1
 
2.4%
90 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
4 1
2.4%
8 1
2.4%
10 1
2.4%
11 1
2.4%
16 1
2.4%
22 1
2.4%
31 1
2.4%
34 1
2.4%
37 1
2.4%
46 1
2.4%
ValueCountFrequency (%)
70134 1
2.4%
56074 1
2.4%
18348 1
2.4%
11204 1
2.4%
11157 1
2.4%
6486 1
2.4%
4382 1
2.4%
3693 1
2.4%
3007 1
2.4%
2145 1
2.4%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.119048
Minimum0
Maximum691
Zeros4
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T15:35:40.043545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8.5
Q339.25
95-th percentile380.6
Maximum691
Range691
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation145.68027
Coefficient of variation (CV)2.2371376
Kurtosis9.6408953
Mean65.119048
Median Absolute Deviation (MAD)7.5
Skewness3.0776764
Sum2735
Variance21222.742
MonotonicityNot monotonic
2023-12-12T15:35:40.181713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 6
 
14.3%
0 4
 
9.5%
1 3
 
7.1%
10 3
 
7.1%
5 2
 
4.8%
19 2
 
4.8%
2 2
 
4.8%
386 1
 
2.4%
46 1
 
2.4%
8 1
 
2.4%
Other values (17) 17
40.5%
ValueCountFrequency (%)
0 4
9.5%
1 3
7.1%
2 2
 
4.8%
3 6
14.3%
4 1
 
2.4%
5 2
 
4.8%
6 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
ValueCountFrequency (%)
691 1
2.4%
508 1
2.4%
386 1
2.4%
278 1
2.4%
200 1
2.4%
152 1
2.4%
102 1
2.4%
53 1
2.4%
50 1
2.4%
46 1
2.4%

중상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1231.3095
Minimum2
Maximum16602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T15:35:40.354276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q117.75
median100
Q3483
95-th percentile5079.3
Maximum16602
Range16600
Interquartile range (IQR)465.25

Descriptive statistics

Standard deviation3453.7315
Coefficient of variation (CV)2.8049255
Kurtosis14.714657
Mean1231.3095
Median Absolute Deviation (MAD)88
Skewness3.841524
Sum51715
Variance11928261
MonotonicityNot monotonic
2023-12-12T15:35:40.521372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
105 2
 
4.8%
12 2
 
4.8%
2 2
 
4.8%
109 2
 
4.8%
3 2
 
4.8%
5163 1
 
2.4%
1259 1
 
2.4%
2494 1
 
2.4%
91 1
 
2.4%
17 1
 
2.4%
Other values (27) 27
64.3%
ValueCountFrequency (%)
2 2
4.8%
3 2
4.8%
6 1
2.4%
8 1
2.4%
12 2
4.8%
15 1
2.4%
16 1
2.4%
17 1
2.4%
20 1
2.4%
28 1
2.4%
ValueCountFrequency (%)
16602 1
2.4%
14839 1
2.4%
5163 1
2.4%
3489 1
2.4%
2794 1
2.4%
2494 1
2.4%
1259 1
2.4%
905 1
2.4%
854 1
2.4%
539 1
2.4%

경상자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5057.8571
Minimum2
Maximum74568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T15:35:40.654384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.15
Q1100
median405
Q31787.75
95-th percentile20833.6
Maximum74568
Range74566
Interquartile range (IQR)1687.75

Descriptive statistics

Standard deviation14727.129
Coefficient of variation (CV)2.9117328
Kurtosis16.162544
Mean5057.8571
Median Absolute Deviation (MAD)371.5
Skewness4.0078336
Sum212430
Variance2.1688832 × 108
MonotonicityNot monotonic
2023-12-12T15:35:40.785384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
21293 1
 
2.4%
472 1
 
2.4%
6102 1
 
2.4%
225 1
 
2.4%
289 1
 
2.4%
5 1
 
2.4%
59 1
 
2.4%
14 1
 
2.4%
7260 1
 
2.4%
115 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
2 1
2.4%
5 1
2.4%
6 1
2.4%
9 1
2.4%
14 1
2.4%
23 1
2.4%
30 1
2.4%
37 1
2.4%
45 1
2.4%
59 1
2.4%
ValueCountFrequency (%)
74568 1
2.4%
59642 1
2.4%
21293 1
2.4%
12105 1
2.4%
10909 1
2.4%
7260 1
2.4%
6102 1
2.4%
3909 1
2.4%
3173 1
2.4%
2265 1
2.4%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.42857
Minimum0
Maximum5851
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T15:35:40.932257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14.75
median30
Q3152.25
95-th percentile1760.8
Maximum5851
Range5851
Interquartile range (IQR)147.5

Descriptive statistics

Standard deviation1156.9096
Coefficient of variation (CV)2.7517389
Kurtosis15.223719
Mean420.42857
Median Absolute Deviation (MAD)29
Skewness3.8557562
Sum17658
Variance1338439.9
MonotonicityNot monotonic
2023-12-12T15:35:41.068550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2 4
 
9.5%
1 4
 
9.5%
1786 1
 
2.4%
7 1
 
2.4%
25 1
 
2.4%
554 1
 
2.4%
28 1
 
2.4%
24 1
 
2.4%
4 1
 
2.4%
782 1
 
2.4%
Other values (26) 26
61.9%
ValueCountFrequency (%)
0 1
 
2.4%
1 4
9.5%
2 4
9.5%
3 1
 
2.4%
4 1
 
2.4%
7 1
 
2.4%
10 1
 
2.4%
12 1
 
2.4%
14 1
 
2.4%
22 1
 
2.4%
ValueCountFrequency (%)
5851 1
2.4%
4569 1
2.4%
1786 1
2.4%
1282 1
2.4%
1084 1
2.4%
782 1
2.4%
554 1
2.4%
295 1
2.4%
228 1
2.4%
220 1
2.4%

Interactions

2023-12-12T15:35:38.207142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:35.896100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.598065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.107272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.662936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.289459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.004725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.706407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.221656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.781662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.374970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.159925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.801396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.343772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.892898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.463445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.316781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.906864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.452812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.009204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.549337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:36.468710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.017130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.553516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.117000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:35:41.170639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류기상상태사고건수사망자수중상자수경상자수부상신고자수
도로종류1.0000.0000.0000.0000.0000.0000.000
기상상태0.0001.0000.3400.4090.5630.3400.563
사고건수0.0000.3401.0001.0000.9641.0000.964
사망자수0.0000.4091.0001.0000.9781.0000.978
중상자수0.0000.5630.9640.9781.0000.9640.996
경상자수0.0000.3401.0001.0000.9641.0000.964
부상신고자수0.0000.5630.9640.9780.9960.9641.000
2023-12-12T15:35:41.305566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기상상태도로종류
기상상태1.0000.000
도로종류0.0001.000
2023-12-12T15:35:41.424652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고건수사망자수중상자수경상자수부상신고자수도로종류기상상태
사고건수1.0000.9130.9810.9930.9820.0000.228
사망자수0.9131.0000.9300.9090.9150.0000.247
중상자수0.9810.9301.0000.9860.9630.0000.226
경상자수0.9930.9090.9861.0000.9730.0000.228
부상신고자수0.9820.9150.9630.9731.0000.0000.226
도로종류0.0000.0000.0000.0000.0001.0000.000
기상상태0.2280.2470.2260.2280.2260.0001.000

Missing values

2023-12-12T15:35:38.935518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:35:39.042164image/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

도로종류기상상태사고건수사망자수중상자수경상자수부상신고자수
0일반국도맑음183483865163212931786
1일반국도흐림7612723683972
2일반국도비1100403301262108
3일반국도안개34312372
4일반국도눈17874320131
5일반국도기타/불명16755018622
6지방도맑음111572783489121051282
7지방도흐림3961910940834
8지방도비5541517860065
9지방도안개31020302
도로종류기상상태사고건수사망자수중상자수경상자수부상신고자수
32고속국도비3251913447262
33고속국도안개40221
34고속국도눈371321220
35고속국도기타/불명2236231
36기타맑음112041022794109091084
37기타흐림432109540754
38기타비540811656656
39기타안개101361
40기타눈8121211112
41기타기타/불명5371010558361