Overview

Dataset statistics

Number of variables5
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory46.1 B

Variable types

Categorical2
Numeric3

Dataset

Description가해운전자 차종별(전동킥보드, 자전거, 오토바이 등) 2017년부터 2019년까지의 사고건수, 사망자수, 부상자수 현황에 대한 데이터
Author도로교통공단
URLhttps://www.data.go.kr/data/15087990/fileData.do

Alerts

2017 is highly overall correlated with 2018 and 1 other fieldsHigh correlation
2018 is highly overall correlated with 2017 and 1 other fieldsHigh correlation
2019 is highly overall correlated with 2017 and 1 other fieldsHigh correlation
2017 has unique valuesUnique
2018 has unique valuesUnique
2019 has unique valuesUnique
2017 has 1 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-12 01:49:38.090431
Analysis finished2023-12-12 01:49:39.471696
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가해운전자 차종별
Categorical

Distinct14
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size468.0 B
합계
3 
승용차
3 
승합차
3 
화물차
3 
특수차
3 
Other values (9)
27 

Length

Max length11
Median length9.5
Mean length4.3571429
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row합계
2nd row합계
3rd row합계
4th row승용차
5th row승용차

Common Values

ValueCountFrequency (%)
합계 3
 
7.1%
승용차 3
 
7.1%
승합차 3
 
7.1%
화물차 3
 
7.1%
특수차 3
 
7.1%
이륜차 3
 
7.1%
사륜오토바이(ATV) 3
 
7.1%
원동기장치자전거 3
 
7.1%
자전거 3
 
7.1%
개인형이동수단(PM) 3
 
7.1%
Other values (4) 12
28.6%

Length

2023-12-12T10:49:39.563919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합계 3
 
7.1%
승용차 3
 
7.1%
승합차 3
 
7.1%
화물차 3
 
7.1%
특수차 3
 
7.1%
이륜차 3
 
7.1%
사륜오토바이(atv 3
 
7.1%
원동기장치자전거 3
 
7.1%
자전거 3
 
7.1%
개인형이동수단(pm 3
 
7.1%
Other values (4) 12
28.6%

사고년도
Categorical

Distinct3
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size468.0 B
사고건수
14 
사망자수
14 
부상자수
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사고건수
2nd row사망자수
3rd row부상자수
4th row사고건수
5th row사망자수

Common Values

ValueCountFrequency (%)
사고건수 14
33.3%
사망자수 14
33.3%
부상자수 14
33.3%

Length

2023-12-12T10:49:39.721608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:49:39.853294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사고건수 14
33.3%
사망자수 14
33.3%
부상자수 14
33.3%

2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25873.762
Minimum0
Maximum322829
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T10:49:40.014504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.15
Q1134.5
median1373
Q35863.75
95-th percentile212670.3
Maximum322829
Range322829
Interquartile range (IQR)5729.25

Descriptive statistics

Standard deviation69088.938
Coefficient of variation (CV)2.6702317
Kurtosis10.056162
Mean25873.762
Median Absolute Deviation (MAD)1355.5
Skewness3.2297231
Sum1086698
Variance4.7732814 × 109
MonotonicityNot monotonic
2023-12-12T10:49:40.204809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
216335 1
 
2.4%
3737 1
 
2.4%
5659 1
 
2.4%
126 1
 
2.4%
5932 1
 
2.4%
117 1
 
2.4%
4 1
 
2.4%
124 1
 
2.4%
2476 1
 
2.4%
96 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
0 1
2.4%
4 1
2.4%
6 1
2.4%
29 1
2.4%
37 1
2.4%
65 1
2.4%
96 1
2.4%
117 1
2.4%
121 1
2.4%
124 1
2.4%
ValueCountFrequency (%)
322829 1
2.4%
220358 1
2.4%
216335 1
2.4%
143041 1
2.4%
41157 1
2.4%
27341 1
2.4%
22238 1
2.4%
16720 1
2.4%
13730 1
2.4%
13426 1
2.4%

2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25903.143
Minimum1
Maximum323037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T10:49:40.406483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.55
Q1205.5
median1495.5
Q34973.5
95-th percentile213552.5
Maximum323037
Range323036
Interquartile range (IQR)4768

Descriptive statistics

Standard deviation69432.909
Coefficient of variation (CV)2.680482
Kurtosis9.928516
Mean25903.143
Median Absolute Deviation (MAD)1436
Skewness3.2143779
Sum1087932
Variance4.8209289 × 109
MonotonicityNot monotonic
2023-12-12T10:49:40.585228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
217148 1
 
2.4%
3734 1
 
2.4%
4771 1
 
2.4%
91 1
 
2.4%
5041 1
 
2.4%
225 1
 
2.4%
4 1
 
2.4%
238 1
 
2.4%
2427 1
 
2.4%
73 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1 1
2.4%
4 1
2.4%
6 1
2.4%
57 1
2.4%
59 1
2.4%
60 1
2.4%
68 1
2.4%
73 1
2.4%
91 1
2.4%
169 1
2.4%
ValueCountFrequency (%)
323037 1
2.4%
222226 1
2.4%
217148 1
2.4%
145238 1
2.4%
41636 1
2.4%
27562 1
2.4%
21542 1
2.4%
18621 1
2.4%
15032 1
2.4%
13526 1
2.4%

2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27364.81
Minimum1
Maximum341712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T10:49:40.746526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.4
Q1213.25
median1397
Q35923.25
95-th percentile225688.25
Maximum341712
Range341711
Interquartile range (IQR)5710

Descriptive statistics

Standard deviation73151.804
Coefficient of variation (CV)2.6732071
Kurtosis10.035981
Mean27364.81
Median Absolute Deviation (MAD)1358
Skewness3.2247775
Sum1149322
Variance5.3511864 × 109
MonotonicityNot monotonic
2023-12-12T10:49:40.935838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
229600 1
 
2.4%
3803 1
 
2.4%
5633 1
 
2.4%
79 1
 
2.4%
6020 1
 
2.4%
447 1
 
2.4%
8 1
 
2.4%
473 1
 
2.4%
2542 1
 
2.4%
84 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
8 1
2.4%
36 1
2.4%
38 1
2.4%
40 1
2.4%
57 1
2.4%
79 1
2.4%
84 1
2.4%
203 1
2.4%
ValueCountFrequency (%)
341712 1
2.4%
232408 1
2.4%
229600 1
2.4%
151365 1
2.4%
42960 1
2.4%
28788 1
2.4%
23584 1
2.4%
22675 1
2.4%
18467 1
2.4%
14221 1
2.4%

Interactions

2023-12-12T10:49:38.865757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.280228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.581044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.965854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.387773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.678590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:39.102161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.491148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:49:38.766001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:49:41.069291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가해운전자 차종별사고년도201720182019
가해운전자 차종별1.0000.0000.2310.2310.231
사고년도0.0001.0000.0000.0000.000
20170.2310.0001.0001.0001.000
20180.2310.0001.0001.0001.000
20190.2310.0001.0001.0001.000
2023-12-12T10:49:41.182776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고년도가해운전자 차종별
사고년도1.0000.000
가해운전자 차종별0.0001.000
2023-12-12T10:49:41.306878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201720182019가해운전자 차종별사고년도
20171.0000.9930.9810.0550.000
20180.9931.0000.9920.0550.000
20190.9810.9921.0000.0550.000
가해운전자 차종별0.0550.0550.0551.0000.000
사고년도0.0000.0000.0000.0001.000

Missing values

2023-12-12T10:49:39.243043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:49:39.432431image/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

가해운전자 차종별사고년도201720182019
0합계사고건수216335217148229600
1합계사망자수418537813349
2합계부상자수322829323037341712
3승용차사고건수143041145238151365
4승용차사망자수204818371571
5승용차부상자수220358222226232408
6승합차사고건수134261352614221
7승합차사망자수286247209
8승합차부상자수222382154222675
9화물차사고건수273412756228788
가해운전자 차종별사고년도201720182019
32건설기계부상자수373737343803
33농기계사고건수450398444
34농기계사망자수656057
35농기계부상자수491431511
36기타사고건수160169203
37기타사망자수612
38기타부상자수202199226
39불명사고건수437040433836
40불명사망자수061
41불명부상자수481645564238