Overview

Dataset statistics

Number of variables10
Number of observations5225
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory444.0 KiB
Average record size in memory87.0 B

Variable types

Numeric7
Categorical3

Dataset

Description일자별(월-일) 사고유형별(차대차, 차대사람, 차량단독 등) 교통사고 현황(사고건수, 사망자수, 중상자수, 경상자수, 부상신고자수) * 교통사고분석시스템(taas.koroad.or.kr) 데이터를 기반으로 함
Author도로교통공단
URLhttps://www.data.go.kr/data/15105299/fileData.do

Alerts

사고유형대분류 is highly overall correlated with 사고유형중분류 and 1 other fieldsHigh correlation
사고유형중분류 is highly overall correlated with 사고유형대분류 and 1 other fieldsHigh correlation
사고유형 is highly overall correlated with 사고유형대분류 and 1 other fieldsHigh correlation
사고건수 is highly overall correlated with 중상자수 and 2 other fieldsHigh correlation
중상자수 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
경상자수 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
부상신고자수 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
사망자수 has 3282 (62.8%) zerosZeros
중상자수 has 746 (14.3%) zerosZeros
경상자수 has 413 (7.9%) zerosZeros
부상신고자수 has 2042 (39.1%) zerosZeros

Reproduction

Analysis started2023-12-11 23:50:48.662864
Analysis finished2023-12-11 23:50:55.441923
Duration6.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생월
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5278469
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:55.506300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.435865
Coefficient of variation (CV)0.5263397
Kurtosis-1.1981727
Mean6.5278469
Median Absolute Deviation (MAD)3
Skewness-0.010130371
Sum34108
Variance11.805168
MonotonicityIncreasing
2023-12-12T08:50:55.622891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 449
8.6%
8 447
8.6%
5 446
8.5%
10 443
8.5%
3 442
8.5%
12 441
8.4%
6 438
8.4%
1 435
8.3%
9 430
8.2%
4 427
8.2%
Other values (2) 827
15.8%
ValueCountFrequency (%)
1 435
8.3%
2 403
7.7%
3 442
8.5%
4 427
8.2%
5 446
8.5%
6 438
8.4%
7 449
8.6%
8 447
8.6%
9 430
8.2%
10 443
8.5%
ValueCountFrequency (%)
12 441
8.4%
11 424
8.1%
10 443
8.5%
9 430
8.2%
8 447
8.6%
7 449
8.6%
6 438
8.4%
5 446
8.5%
4 427
8.2%
3 442
8.5%

발생일
Real number (ℝ)

Distinct31
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.72823
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:55.734352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8089959
Coefficient of variation (CV)0.56007549
Kurtosis-1.1983853
Mean15.72823
Median Absolute Deviation (MAD)8
Skewness0.0060816352
Sum82180
Variance77.598409
MonotonicityNot monotonic
2023-12-12T08:50:55.857244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 177
 
3.4%
27 175
 
3.3%
24 175
 
3.3%
6 174
 
3.3%
20 174
 
3.3%
8 173
 
3.3%
16 173
 
3.3%
22 173
 
3.3%
12 172
 
3.3%
28 172
 
3.3%
Other values (21) 3487
66.7%
ValueCountFrequency (%)
1 171
3.3%
2 172
3.3%
3 171
3.3%
4 177
3.4%
5 171
3.3%
6 174
3.3%
7 170
3.3%
8 173
3.3%
9 169
3.2%
10 171
3.3%
ValueCountFrequency (%)
31 98
1.9%
30 161
3.1%
29 158
3.0%
28 172
3.3%
27 175
3.3%
26 172
3.3%
25 169
3.2%
24 175
3.3%
23 171
3.3%
22 173
3.3%

사고유형대분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.9 KiB
차대차
1825 
차대사람
1821 
차량단독
1578 
철길건널목
 
1

Length

Max length5
Median length4
Mean length3.6509091
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
차대차 1825
34.9%
차대사람 1821
34.9%
차량단독 1578
30.2%
철길건널목 1
 
< 0.1%

Length

2023-12-12T08:50:56.006027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:50:56.131090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차대차 1825
34.9%
차대사람 1821
34.9%
차량단독 1578
30.2%
철길건널목 1
 
< 0.1%

사고유형중분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size40.9 KiB
기타
1095 
횡단중
365 
차도통행중
365 
정면충돌
365 
측면충돌
365 
Other values (10)
2670 

Length

Max length10
Median length9
Mean length3.9119617
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row횡단중
2nd row차도통행중
3rd row길가장자리구역통행중
4th row보도통행중
5th row기타

Common Values

ValueCountFrequency (%)
기타 1095
21.0%
횡단중 365
 
7.0%
차도통행중 365
 
7.0%
정면충돌 365
 
7.0%
측면충돌 365
 
7.0%
후진중충돌 365
 
7.0%
추돌 365
 
7.0%
공작물충돌 364
 
7.0%
길가장자리구역통행중 363
 
6.9%
보도통행중 363
 
6.9%
Other values (5) 850
16.3%

Length

2023-12-12T08:50:56.244785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 1095
20.9%
횡단중 365
 
7.0%
차도통행중 365
 
7.0%
정면충돌 365
 
7.0%
측면충돌 365
 
7.0%
후진중충돌 365
 
7.0%
추돌 365
 
7.0%
공작물충돌 364
 
6.9%
길가장자리구역통행중 363
 
6.9%
보도통행중 363
 
6.9%
Other values (6) 873
16.6%

사고유형
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size40.9 KiB
기타
1095 
횡단중
365 
차도통행중
365 
정면충돌
365 
측면충돌
365 
Other values (11)
2670 

Length

Max length10
Median length9
Mean length4.1203828
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row횡단중
2nd row차도통행중
3rd row길가장자리구역통행중
4th row보도통행중
5th row기타

Common Values

ValueCountFrequency (%)
기타 1095
21.0%
횡단중 365
 
7.0%
차도통행중 365
 
7.0%
정면충돌 365
 
7.0%
측면충돌 365
 
7.0%
후진중충돌 365
 
7.0%
추돌 365
 
7.0%
공작물충돌 364
 
7.0%
길가장자리구역통행중 363
 
6.9%
보도통행중 363
 
6.9%
Other values (6) 850
16.3%

Length

2023-12-12T08:50:56.365391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 1219
21.7%
차도통행중 365
 
6.5%
정면충돌 365
 
6.5%
측면충돌 365
 
6.5%
후진중충돌 365
 
6.5%
추돌 365
 
6.5%
횡단중 365
 
6.5%
공작물충돌 364
 
6.5%
도로이탈 363
 
6.5%
길가장자리구역통행중 363
 
6.5%
Other values (7) 1112
19.8%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct253
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.876555
Minimum1
Maximum283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:56.479152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q344
95-th percentile180.8
Maximum283
Range282
Interquartile range (IQR)39

Descriptive statistics

Standard deviation56.447022
Coefficient of variation (CV)1.4519554
Kurtosis2.9362366
Mean38.876555
Median Absolute Deviation (MAD)9
Skewness1.9307384
Sum203130
Variance3186.2663
MonotonicityNot monotonic
2023-12-12T08:50:56.821289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 469
 
9.0%
6 275
 
5.3%
2 264
 
5.1%
8 252
 
4.8%
7 246
 
4.7%
3 232
 
4.4%
9 219
 
4.2%
5 204
 
3.9%
4 200
 
3.8%
10 179
 
3.4%
Other values (243) 2685
51.4%
ValueCountFrequency (%)
1 469
9.0%
2 264
5.1%
3 232
4.4%
4 200
3.8%
5 204
3.9%
6 275
5.3%
7 246
4.7%
8 252
4.8%
9 219
4.2%
10 179
 
3.4%
ValueCountFrequency (%)
283 1
 
< 0.1%
267 2
< 0.1%
266 1
 
< 0.1%
265 1
 
< 0.1%
261 1
 
< 0.1%
254 1
 
< 0.1%
253 2
< 0.1%
252 1
 
< 0.1%
250 1
 
< 0.1%
248 3
0.1%

사망자수
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55808612
Minimum0
Maximum7
Zeros3282
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:56.906812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87925523
Coefficient of variation (CV)1.575483
Kurtosis4.4547838
Mean0.55808612
Median Absolute Deviation (MAD)0
Skewness1.9129059
Sum2916
Variance0.77308975
MonotonicityNot monotonic
2023-12-12T08:50:56.996158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3282
62.8%
1 1259
 
24.1%
2 481
 
9.2%
3 138
 
2.6%
4 49
 
0.9%
5 12
 
0.2%
6 3
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 3282
62.8%
1 1259
 
24.1%
2 481
 
9.2%
3 138
 
2.6%
4 49
 
0.9%
5 12
 
0.2%
6 3
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 3
 
0.1%
5 12
 
0.2%
4 49
 
0.9%
3 138
 
2.6%
2 481
 
9.2%
1 1259
 
24.1%
0 3282
62.8%

중상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.699522
Minimum0
Maximum75
Zeros746
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:57.116396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q316
95-th percentile43
Maximum75
Range75
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.956467
Coefficient of variation (CV)1.304401
Kurtosis3.1524627
Mean10.699522
Median Absolute Deviation (MAD)4
Skewness1.8348036
Sum55905
Variance194.78297
MonotonicityNot monotonic
2023-12-12T08:50:57.225863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 796
15.2%
0 746
 
14.3%
2 532
 
10.2%
3 389
 
7.4%
4 259
 
5.0%
5 176
 
3.4%
6 149
 
2.9%
7 105
 
2.0%
10 97
 
1.9%
16 96
 
1.8%
Other values (65) 1880
36.0%
ValueCountFrequency (%)
0 746
14.3%
1 796
15.2%
2 532
10.2%
3 389
7.4%
4 259
 
5.0%
5 176
 
3.4%
6 149
 
2.9%
7 105
 
2.0%
8 91
 
1.7%
9 85
 
1.6%
ValueCountFrequency (%)
75 1
 
< 0.1%
74 1
 
< 0.1%
73 1
 
< 0.1%
71 2
 
< 0.1%
70 1
 
< 0.1%
69 6
0.1%
68 4
0.1%
67 3
0.1%
66 2
 
< 0.1%
65 2
 
< 0.1%

경상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct271
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.968038
Minimum0
Maximum348
Zeros413
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:57.336147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q329
95-th percentile214
Maximum348
Range348
Interquartile range (IQR)26

Descriptive statistics

Standard deviation69.183019
Coefficient of variation (CV)1.6484692
Kurtosis2.5623221
Mean41.968038
Median Absolute Deviation (MAD)7
Skewness1.9025954
Sum219283
Variance4786.2901
MonotonicityNot monotonic
2023-12-12T08:50:57.447438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 464
 
8.9%
0 413
 
7.9%
2 338
 
6.5%
3 311
 
6.0%
4 303
 
5.8%
5 272
 
5.2%
6 231
 
4.4%
7 182
 
3.5%
8 133
 
2.5%
9 96
 
1.8%
Other values (261) 2482
47.5%
ValueCountFrequency (%)
0 413
7.9%
1 464
8.9%
2 338
6.5%
3 311
6.0%
4 303
5.8%
5 272
5.2%
6 231
4.4%
7 182
 
3.5%
8 133
 
2.5%
9 96
 
1.8%
ValueCountFrequency (%)
348 1
 
< 0.1%
311 1
 
< 0.1%
310 1
 
< 0.1%
309 1
 
< 0.1%
307 1
 
< 0.1%
306 1
 
< 0.1%
304 4
0.1%
298 1
 
< 0.1%
297 1
 
< 0.1%
295 1
 
< 0.1%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1425837
Minimum0
Maximum34
Zeros2042
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size46.1 KiB
2023-12-12T08:50:57.616449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile15
Maximum34
Range34
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.0593865
Coefficient of variation (CV)1.6099449
Kurtosis4.7686026
Mean3.1425837
Median Absolute Deviation (MAD)1
Skewness2.2053131
Sum16420
Variance25.597392
MonotonicityNot monotonic
2023-12-12T08:50:57.799383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 2042
39.1%
1 1060
20.3%
2 564
 
10.8%
3 278
 
5.3%
4 173
 
3.3%
5 110
 
2.1%
6 104
 
2.0%
8 104
 
2.0%
7 86
 
1.6%
10 84
 
1.6%
Other values (23) 620
 
11.9%
ValueCountFrequency (%)
0 2042
39.1%
1 1060
20.3%
2 564
 
10.8%
3 278
 
5.3%
4 173
 
3.3%
5 110
 
2.1%
6 104
 
2.0%
7 86
 
1.6%
8 104
 
2.0%
9 79
 
1.5%
ValueCountFrequency (%)
34 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 2
 
< 0.1%
28 6
0.1%
27 3
 
0.1%
26 5
0.1%
25 1
 
< 0.1%
24 6
0.1%
23 10
0.2%

Interactions

2023-12-12T08:50:54.299448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:49.670632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:50.651981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.286129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.893263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.537872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.402374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.434716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:49.747941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:50.734673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.367677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.971503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.683857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.530456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.557579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:49.833611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:50.819048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.463559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.052561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.792421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.695098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.680124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:49.913689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:50.898883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.550400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.133311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.889429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.836700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.812032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:49.995732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.002910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.635965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.222469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.014824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.959602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.947175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:50.089476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.112204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.715195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.303647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.138584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.075523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:55.074116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:50.215372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.199488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:51.798393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:52.391255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:53.271109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:50:54.179800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:50:57.939458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월발생일사고유형대분류사고유형중분류사고유형사고건수사망자수중상자수경상자수부상신고자수
발생월1.0000.0000.0000.0000.0000.0470.0250.0000.0840.000
발생일0.0001.0000.0000.0000.0000.0000.0230.0000.0000.000
사고유형대분류0.0000.0001.0000.9700.9970.6580.1410.5860.6040.550
사고유형중분류0.0000.0000.9701.0001.0000.8160.4020.7760.7610.664
사고유형0.0000.0000.9971.0001.0000.7980.4730.7560.7420.644
사고건수0.0470.0000.6580.8160.7981.0000.2980.9000.9330.826
사망자수0.0250.0230.1410.4020.4730.2981.0000.3040.2100.232
중상자수0.0000.0000.5860.7760.7560.9000.3041.0000.8440.770
경상자수0.0840.0000.6040.7610.7420.9330.2100.8441.0000.841
부상신고자수0.0000.0000.5500.6640.6440.8260.2320.7700.8411.000
2023-12-12T08:50:58.136117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고유형대분류사고유형중분류사고유형
사고유형대분류1.0000.9260.926
사고유형중분류0.9261.0001.000
사고유형0.9261.0001.000
2023-12-12T08:50:58.257478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월발생일사고건수사망자수중상자수경상자수부상신고자수사고유형대분류사고유형중분류사고유형
발생월1.0000.0060.0260.0610.0260.0190.0220.0000.0000.000
발생일0.0061.0000.003-0.006-0.0020.004-0.0020.0000.0000.000
사고건수0.0260.0031.0000.3840.9080.9620.7530.4580.4710.471
사망자수0.061-0.0060.3841.0000.3960.3100.3070.0640.1840.184
중상자수0.026-0.0020.9080.3961.0000.8240.7040.3910.4200.420
경상자수0.0190.0040.9620.3100.8241.0000.6970.4080.4050.404
부상신고자수0.022-0.0020.7530.3070.7040.6971.0000.3610.3160.316
사고유형대분류0.0000.0000.4580.0640.3910.4080.3611.0000.9260.926
사고유형중분류0.0000.0000.4710.1840.4200.4050.3160.9261.0001.000
사고유형0.0000.0000.4710.1840.4200.4040.3160.9261.0001.000

Missing values

2023-12-12T08:50:55.227715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:50:55.380281image/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

발생월발생일사고유형대분류사고유형중분류사고유형사고건수사망자수중상자수경상자수부상신고자수
011차대사람횡단중횡단중112360
111차대사람차도통행중차도통행중20020
211차대사람길가장자리구역통행중길가장자리구역통행중10010
311차대사람보도통행중보도통행중40130
411차대사람기타기타142490
511차대차정면충돌정면충돌1419141
611차대차측면충돌측면충돌901221133
711차대차후진중충돌후진중충돌60251
811차대차추돌추돌50115792
911차대차기타기타48013529
발생월발생일사고유형대분류사고유형중분류사고유형사고건수사망자수중상자수경상자수부상신고자수
52151231차대사람보도통행중보도통행중10100
52161231차대사람기타기타41113252
52171231차대차정면충돌정면충돌2218234
52181231차대차측면충돌측면충돌19004223522
52191231차대차후진중충돌후진중충돌1000121
52201231차대차추돌추돌13012420919
52211231차대차기타기타14114117514
52221231차량단독전복전복11000
52231231차량단독공작물충돌공작물충돌30021
52241231차량단독기타기타30021