Overview

Dataset statistics

Number of variables7
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory63.6 B

Variable types

Categorical1
Numeric6

Dataset

Description- 도로종류별(일반국도, 고속국도 등), 월별 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/15070271/fileData.do

Alerts

사고건수 is highly overall correlated with 사망자수 and 4 other fieldsHigh correlation
사망자수 is highly overall correlated with 사고건수 and 4 other fieldsHigh correlation
중상자수 is highly overall correlated with 사고건수 and 4 other fieldsHigh correlation
경상자수 is highly overall correlated with 사고건수 and 4 other fieldsHigh correlation
부상신고자수 is highly overall correlated with 사고건수 and 4 other fieldsHigh correlation
도로종류 is highly overall correlated with 사고건수 and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 12:45:02.372887
Analysis finished2023-12-12 12:45:06.554494
Duration4.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size804.0 B
일반국도
12 
지방도
12 
특별광역시도
12 
시도
12 
군도
12 
Other values (2)
24 

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 12
14.3%
지방도 12
14.3%
특별광역시도 12
14.3%
시도 12
14.3%
군도 12
14.3%
고속국도 12
14.3%
기타 12
14.3%

Length

2023-12-12T21:45:06.663393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:45:06.828619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반국도 12
14.3%
지방도 12
14.3%
특별광역시도 12
14.3%
시도 12
14.3%
군도 12
14.3%
고속국도 12
14.3%
기타 12
14.3%

발생월
Real number (ℝ)

Distinct12
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T21:45:06.972967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4727858
Coefficient of variation (CV)0.53427473
Kurtosis-1.2174655
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum546
Variance12.060241
MonotonicityNot monotonic
2023-12-12T21:45:07.103010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7
8.3%
2 7
8.3%
3 7
8.3%
4 7
8.3%
5 7
8.3%
6 7
8.3%
7 7
8.3%
8 7
8.3%
9 7
8.3%
10 7
8.3%
Other values (2) 14
16.7%
ValueCountFrequency (%)
1 7
8.3%
2 7
8.3%
3 7
8.3%
4 7
8.3%
5 7
8.3%
6 7
8.3%
7 7
8.3%
8 7
8.3%
9 7
8.3%
10 7
8.3%
ValueCountFrequency (%)
12 7
8.3%
11 7
8.3%
10 7
8.3%
9 7
8.3%
8 7
8.3%
7 7
8.3%
6 7
8.3%
5 7
8.3%
4 7
8.3%
3 7
8.3%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2343.2857
Minimum269
Maximum7169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T21:45:07.243383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum269
5-th percentile386.6
Q1664
median1142
Q34979.25
95-th percentile6744.4
Maximum7169
Range6900
Interquartile range (IQR)4315.25

Descriptive statistics

Standard deviation2283.7373
Coefficient of variation (CV)0.97458764
Kurtosis-0.69508251
Mean2343.2857
Median Absolute Deviation (MAD)651
Skewness0.99927718
Sum196836
Variance5215456.1
MonotonicityNot monotonic
2023-12-12T21:45:07.417425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
386 2
 
2.4%
1561 1
 
1.2%
638 1
 
1.2%
320 1
 
1.2%
400 1
 
1.2%
452 1
 
1.2%
608 1
 
1.2%
686 1
 
1.2%
651 1
 
1.2%
678 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
269 1
1.2%
320 1
1.2%
370 1
1.2%
386 2
2.4%
390 1
1.2%
400 1
1.2%
423 1
1.2%
424 1
1.2%
432 1
1.2%
452 1
1.2%
ValueCountFrequency (%)
7169 1
1.2%
7119 1
1.2%
6803 1
1.2%
6771 1
1.2%
6745 1
1.2%
6741 1
1.2%
6506 1
1.2%
6496 1
1.2%
6488 1
1.2%
6210 1
1.2%

사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.559524
Minimum5
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T21:45:07.549372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q115.75
median27.5
Q350
95-th percentile71.7
Maximum84
Range79
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation20.297398
Coefficient of variation (CV)0.62339358
Kurtosis-0.60767206
Mean32.559524
Median Absolute Deviation (MAD)14
Skewness0.664125
Sum2735
Variance411.98437
MonotonicityNot monotonic
2023-12-12T21:45:07.697212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
37 4
 
4.8%
13 4
 
4.8%
8 4
 
4.8%
18 4
 
4.8%
15 4
 
4.8%
36 3
 
3.6%
28 3
 
3.6%
10 3
 
3.6%
16 3
 
3.6%
20 2
 
2.4%
Other values (38) 50
59.5%
ValueCountFrequency (%)
5 1
 
1.2%
7 2
2.4%
8 4
4.8%
10 3
3.6%
11 1
 
1.2%
13 4
4.8%
14 2
2.4%
15 4
4.8%
16 3
3.6%
17 1
 
1.2%
ValueCountFrequency (%)
84 1
1.2%
74 2
2.4%
73 1
1.2%
72 1
1.2%
70 2
2.4%
68 1
1.2%
59 2
2.4%
58 1
1.2%
57 1
1.2%
56 1
1.2%

중상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean615.65476
Minimum86
Maximum1720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T21:45:07.824332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile117.9
Q1237.25
median326
Q31242
95-th percentile1569.25
Maximum1720
Range1634
Interquartile range (IQR)1004.75

Descriptive statistics

Standard deviation546.33653
Coefficient of variation (CV)0.8874073
Kurtosis-0.92469051
Mean615.65476
Median Absolute Deviation (MAD)165.5
Skewness0.90983231
Sum51715
Variance298483.6
MonotonicityNot monotonic
2023-12-12T21:45:07.954438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
289 3
 
3.6%
125 2
 
2.4%
326 2
 
2.4%
481 1
 
1.2%
257 1
 
1.2%
238 1
 
1.2%
248 1
 
1.2%
275 1
 
1.2%
256 1
 
1.2%
260 1
 
1.2%
Other values (70) 70
83.3%
ValueCountFrequency (%)
86 1
1.2%
92 1
1.2%
96 1
1.2%
115 1
1.2%
117 1
1.2%
123 1
1.2%
124 1
1.2%
125 2
2.4%
130 1
1.2%
139 1
1.2%
ValueCountFrequency (%)
1720 1
1.2%
1660 1
1.2%
1650 1
1.2%
1572 1
1.2%
1570 1
1.2%
1565 1
1.2%
1551 1
1.2%
1534 1
1.2%
1518 1
1.2%
1504 1
1.2%

경상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2528.9286
Minimum427
Maximum7595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T21:45:08.330516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum427
5-th percentile474.35
Q1728.25
median1213
Q35278.25
95-th percentile7188.75
Maximum7595
Range7168
Interquartile range (IQR)4550

Descriptive statistics

Standard deviation2412.8879
Coefficient of variation (CV)0.95411469
Kurtosis-0.67992759
Mean2528.9286
Median Absolute Deviation (MAD)641
Skewness1.0007801
Sum212430
Variance5822028
MonotonicityNot monotonic
2023-12-12T21:45:08.481037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
568 2
 
2.4%
576 1
 
1.2%
482 1
 
1.2%
638 1
 
1.2%
473 1
 
1.2%
488 1
 
1.2%
656 1
 
1.2%
599 1
 
1.2%
672 1
 
1.2%
652 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
427 1
1.2%
434 1
1.2%
452 1
1.2%
461 1
1.2%
473 1
1.2%
482 1
1.2%
488 1
1.2%
568 2
2.4%
576 1
1.2%
588 1
1.2%
ValueCountFrequency (%)
7595 1
1.2%
7575 1
1.2%
7368 1
1.2%
7248 1
1.2%
7203 1
1.2%
7108 1
1.2%
7022 1
1.2%
6949 1
1.2%
6834 1
1.2%
6722 1
1.2%

부상신고자수
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.21429
Minimum28
Maximum628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T21:45:08.608981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile41.15
Q176.5
median129.5
Q3358.25
95-th percentile577.6
Maximum628
Range600
Interquartile range (IQR)281.75

Descriptive statistics

Standard deviation181.94042
Coefficient of variation (CV)0.86549977
Kurtosis-0.49053088
Mean210.21429
Median Absolute Deviation (MAD)68
Skewness1.0163068
Sum17658
Variance33102.315
MonotonicityNot monotonic
2023-12-12T21:45:08.737653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 4
 
4.8%
77 3
 
3.6%
57 3
 
3.6%
59 3
 
3.6%
67 2
 
2.4%
108 2
 
2.4%
186 2
 
2.4%
131 2
 
2.4%
85 2
 
2.4%
75 1
 
1.2%
Other values (60) 60
71.4%
ValueCountFrequency (%)
28 1
1.2%
30 1
1.2%
32 1
1.2%
35 1
1.2%
41 1
1.2%
42 1
1.2%
47 1
1.2%
51 1
1.2%
52 1
1.2%
55 1
1.2%
ValueCountFrequency (%)
628 1
1.2%
595 1
1.2%
591 1
1.2%
587 1
1.2%
583 1
1.2%
547 1
1.2%
540 1
1.2%
534 1
1.2%
521 1
1.2%
520 1
1.2%

Interactions

2023-12-12T21:45:05.810101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:02.863706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.421219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.996958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.615732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.257425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.897743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:02.946341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.532026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.078182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.700888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.354141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.991504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.040865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.622248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.226701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.802630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.449266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:06.094045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.137246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.712620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.320989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.916758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.537379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:06.184859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.232214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.813118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.436976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.044942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.634614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:06.275311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.332309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:03.905816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:04.524225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.151082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:05.716938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:45:08.823970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류발생월사고건수사망자수중상자수경상자수부상신고자수
도로종류1.0000.0000.8610.7550.8110.8280.819
발생월0.0001.0000.0000.0000.0000.0000.000
사고건수0.8610.0001.0000.7160.9660.9890.927
사망자수0.7550.0000.7161.0000.6810.7330.705
중상자수0.8110.0000.9660.6811.0000.9630.857
경상자수0.8280.0000.9890.7330.9631.0000.886
부상신고자수0.8190.0000.9270.7050.8570.8861.000
2023-12-12T21:45:08.935436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월사고건수사망자수중상자수경상자수부상신고자수도로종류
발생월1.0000.1360.2170.0910.1590.1730.000
사고건수0.1361.0000.8000.9730.9750.9500.683
사망자수0.2170.8001.0000.8520.8170.8000.503
중상자수0.0910.9730.8521.0000.9500.9270.604
경상자수0.1590.9750.8170.9501.0000.9720.631
부상신고자수0.1730.9500.8000.9270.9721.0000.604
도로종류0.0000.6830.5030.6040.6310.6041.000

Missing values

2023-12-12T21:45:06.386337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:45:06.513456image/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

도로종류발생월사고건수사망자수중상자수경상자수부상신고자수
0일반국도11561374811787134
1일반국도21307253791456140
2일반국도31459364021640108
3일반국도41682344511992177
4일반국도51856365642103202
5일반국도61711535141883186
6일반국도71771284772092207
7일반국도81756364672058180
8일반국도91787554932140156
9일반국도102018515992339173
도로종류발생월사고건수사망자수중상자수경상자수부상신고자수
74기타3817820576959
75기타41010828297787
76기타5125773261224117
77기타6111182961043108
78기타71155152891147131
79기타81129142511109117
80기타91160132891125149
81기타10131883131338144
82기타111172152641086137
83기타121125192251212107