Overview

Dataset statistics

Number of variables7
Number of observations144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory62.9 B

Variable types

Categorical1
Numeric6

Dataset

Description- 가해운전자 차종별(승용차, 승합차 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/15070209/fileData.do

Alerts

사고건수 is highly overall correlated with 사망자수 and 4 other fieldsHigh correlation
사망자수 is highly overall correlated with 사고건수 and 3 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
가해자차종 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
사망자수 has 13 (9.0%) zerosZeros
부상신고자수 has 4 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-11 23:34:38.661178
Analysis finished2023-12-11 23:34:41.837115
Duration3.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가해자차종
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
승용차
12 
승합차
12 
화물차
12 
특수차
12 
이륜차
12 
Other values (7)
84 

Length

Max length11
Median length3
Mean length5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
승용차 12
8.3%
승합차 12
8.3%
화물차 12
8.3%
특수차 12
8.3%
이륜차 12
8.3%
사륜오토바이(ATV) 12
8.3%
원동기장치자전거 12
8.3%
자전거 12
8.3%
개인형이동장치(PM) 12
8.3%
건설기계 12
8.3%
Other values (2) 24
16.7%

Length

2023-12-12T08:34:41.909405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용차 12
8.3%
승합차 12
8.3%
화물차 12
8.3%
특수차 12
8.3%
이륜차 12
8.3%
사륜오토바이(atv 12
8.3%
원동기장치자전거 12
8.3%
자전거 12
8.3%
개인형이동장치(pm 12
8.3%
건설기계 12
8.3%
Other values (2) 24
16.7%

발생월
Real number (ℝ)

Distinct12
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:34:42.029358image/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 deviation3.4641016
Coefficient of variation (CV)0.53293871
Kurtosis-1.217261
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum936
Variance12
MonotonicityIncreasing
2023-12-12T08:34:42.139025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 12
8.3%
2 12
8.3%
3 12
8.3%
4 12
8.3%
5 12
8.3%
6 12
8.3%
7 12
8.3%
8 12
8.3%
9 12
8.3%
10 12
8.3%
Other values (2) 24
16.7%
ValueCountFrequency (%)
1 12
8.3%
2 12
8.3%
3 12
8.3%
4 12
8.3%
5 12
8.3%
6 12
8.3%
7 12
8.3%
8 12
8.3%
9 12
8.3%
10 12
8.3%
ValueCountFrequency (%)
12 12
8.3%
11 12
8.3%
10 12
8.3%
9 12
8.3%
8 12
8.3%
7 12
8.3%
6 12
8.3%
5 12
8.3%
4 12
8.3%
3 12
8.3%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1366.9167
Minimum8
Maximum12104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:34:42.254521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile20.15
Q1105.25
median216.5
Q31070
95-th percentile10733.25
Maximum12104
Range12096
Interquartile range (IQR)964.75

Descriptive statistics

Standard deviation2935.1338
Coefficient of variation (CV)2.1472661
Kurtosis6.9924251
Mean1366.9167
Median Absolute Deviation (MAD)188
Skewness2.8899961
Sum196836
Variance8615010.3
MonotonicityNot monotonic
2023-12-12T08:34:42.394552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 3
 
2.1%
35 2
 
1.4%
1070 2
 
1.4%
193 2
 
1.4%
26 2
 
1.4%
168 2
 
1.4%
229 2
 
1.4%
34 2
 
1.4%
201 2
 
1.4%
52 2
 
1.4%
Other values (117) 123
85.4%
ValueCountFrequency (%)
8 1
0.7%
11 1
0.7%
12 1
0.7%
13 1
0.7%
15 2
1.4%
18 1
0.7%
20 1
0.7%
21 1
0.7%
22 1
0.7%
23 1
0.7%
ValueCountFrequency (%)
12104 1
0.7%
11803 1
0.7%
11595 1
0.7%
11575 1
0.7%
11224 1
0.7%
11162 1
0.7%
10763 1
0.7%
10734 1
0.7%
10729 1
0.7%
10713 1
0.7%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.993056
Minimum0
Maximum132
Zeros13
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:34:42.529494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q320
95-th percentile93.5
Maximum132
Range132
Interquartile range (IQR)18

Descriptive statistics

Standard deviation29.461483
Coefficient of variation (CV)1.5511713
Kurtosis3.6739005
Mean18.993056
Median Absolute Deviation (MAD)4
Skewness2.0836171
Sum2735
Variance867.97897
MonotonicityNot monotonic
2023-12-12T08:34:42.666749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 16
 
11.1%
4 15
 
10.4%
5 15
 
10.4%
0 13
 
9.0%
1 10
 
6.9%
6 9
 
6.2%
3 8
 
5.6%
10 6
 
4.2%
7 4
 
2.8%
26 2
 
1.4%
Other values (40) 46
31.9%
ValueCountFrequency (%)
0 13
9.0%
1 10
6.9%
2 16
11.1%
3 8
5.6%
4 15
10.4%
5 15
10.4%
6 9
6.2%
7 4
 
2.8%
8 1
 
0.7%
9 1
 
0.7%
ValueCountFrequency (%)
132 1
0.7%
117 1
0.7%
114 2
1.4%
113 1
0.7%
101 1
0.7%
98 1
0.7%
95 1
0.7%
85 1
0.7%
80 1
0.7%
77 1
0.7%

중상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.13194
Minimum0
Maximum2951
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:34:42.797884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q124.5
median61
Q3321.75
95-th percentile2681.6
Maximum2951
Range2951
Interquartile range (IQR)297.25

Descriptive statistics

Standard deviation721.12538
Coefficient of variation (CV)2.0079678
Kurtosis6.4737893
Mean359.13194
Median Absolute Deviation (MAD)49.5
Skewness2.7698624
Sum51715
Variance520021.81
MonotonicityNot monotonic
2023-12-12T08:34:42.944169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 4
 
2.8%
64 4
 
2.8%
29 4
 
2.8%
25 4
 
2.8%
19 4
 
2.8%
14 3
 
2.1%
7 3
 
2.1%
5 3
 
2.1%
9 3
 
2.1%
41 2
 
1.4%
Other values (95) 110
76.4%
ValueCountFrequency (%)
0 1
 
0.7%
4 2
1.4%
5 3
2.1%
7 3
2.1%
8 1
 
0.7%
9 3
2.1%
10 2
1.4%
11 1
 
0.7%
12 4
2.8%
14 3
2.1%
ValueCountFrequency (%)
2951 1
0.7%
2931 1
0.7%
2760 1
0.7%
2754 1
0.7%
2750 1
0.7%
2718 1
0.7%
2707 1
0.7%
2690 1
0.7%
2634 1
0.7%
2623 1
0.7%

경상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1475.2083
Minimum6
Maximum14176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:34:43.093060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.15
Q1110.5
median175.5
Q31060.5
95-th percentile12425.9
Maximum14176
Range14170
Interquartile range (IQR)950

Descriptive statistics

Standard deviation3406.2521
Coefficient of variation (CV)2.3089973
Kurtosis7.2286607
Mean1475.2083
Median Absolute Deviation (MAD)152.5
Skewness2.9406048
Sum212430
Variance11602553
MonotonicityNot monotonic
2023-12-12T08:34:43.254812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 3
 
2.1%
157 3
 
2.1%
11 3
 
2.1%
140 3
 
2.1%
142 2
 
1.4%
116 2
 
1.4%
6 2
 
1.4%
131 2
 
1.4%
7 2
 
1.4%
122 2
 
1.4%
Other values (111) 120
83.3%
ValueCountFrequency (%)
6 2
1.4%
7 2
1.4%
10 1
 
0.7%
11 3
2.1%
12 3
2.1%
13 1
 
0.7%
14 2
1.4%
15 2
1.4%
17 2
1.4%
19 1
 
0.7%
ValueCountFrequency (%)
14176 1
0.7%
13830 1
0.7%
13346 1
0.7%
13138 1
0.7%
13068 1
0.7%
12954 1
0.7%
12659 1
0.7%
12440 1
0.7%
12346 1
0.7%
12176 1
0.7%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.625
Minimum0
Maximum950
Zeros4
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:34:43.420530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median46.5
Q3114.25
95-th percentile804.6
Maximum950
Range950
Interquartile range (IQR)107.25

Descriptive statistics

Standard deviation220.31389
Coefficient of variation (CV)1.7966474
Kurtosis6.6563708
Mean122.625
Median Absolute Deviation (MAD)42.5
Skewness2.7571743
Sum17658
Variance48538.208
MonotonicityNot monotonic
2023-12-12T08:34:43.560425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
4.9%
4 6
 
4.2%
5 5
 
3.5%
7 5
 
3.5%
6 5
 
3.5%
22 4
 
2.8%
10 4
 
2.8%
0 4
 
2.8%
2 4
 
2.8%
3 4
 
2.8%
Other values (82) 96
66.7%
ValueCountFrequency (%)
0 4
2.8%
1 7
4.9%
2 4
2.8%
3 4
2.8%
4 6
4.2%
5 5
3.5%
6 5
3.5%
7 5
3.5%
8 1
 
0.7%
10 4
2.8%
ValueCountFrequency (%)
950 1
0.7%
911 1
0.7%
910 1
0.7%
888 1
0.7%
881 1
0.7%
849 2
1.4%
807 1
0.7%
791 1
0.7%
735 1
0.7%
610 1
0.7%

Interactions

2023-12-12T08:34:41.220006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:38.866894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.249145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.655380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.070556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.511775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.301455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:38.927867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.314414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.736501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.134481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.583445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.374017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:38.988515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.381630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.811954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.199789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.656724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.446321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.063848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.446044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.878804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.269090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.732154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.517656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.127753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.513094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.944094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.358034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.085783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.590547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.193665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:39.579550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.010771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:40.447479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:41.155794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:34:43.650629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가해자차종발생월사고건수사망자수중상자수경상자수부상신고자수
가해자차종1.0000.0000.8970.7780.8210.9070.810
발생월0.0001.0000.0000.0000.0000.0000.000
사고건수0.8970.0001.0000.9420.9350.9990.900
사망자수0.7780.0000.9421.0000.8980.9340.870
중상자수0.8210.0000.9350.8981.0000.9300.954
경상자수0.9070.0000.9990.9340.9301.0000.881
부상신고자수0.8100.0000.9000.8700.9540.8811.000
2023-12-12T08:34:43.776810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월사고건수사망자수중상자수경상자수부상신고자수가해자차종
발생월1.0000.0800.0730.0630.0890.0740.000
사고건수0.0801.0000.7560.9650.9760.9590.562
사망자수0.0730.7561.0000.8240.7790.6860.467
중상자수0.0630.9650.8241.0000.9530.9080.579
경상자수0.0890.9760.7790.9531.0000.9100.579
부상신고자수0.0740.9590.6860.9080.9101.0000.561
가해자차종0.0000.5620.4670.5790.5790.5611.000

Missing values

2023-12-12T08:34:41.685596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:34:41.787475image/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

가해자차종발생월사고건수사망자수중상자수경상자수부상신고자수
0승용차11073498275412346735
1승합차1781524578262
2화물차12020436212176118
3특수차1893291113
4이륜차11360253931120213
5사륜오토바이(ATV)11514132
6원동기장치자전거115563511931
7자전거125357115455
8개인형이동장치(PM)1740105914
9건설기계118254819910
가해자차종발생월사고건수사망자수중상자수경상자수부상신고자수
134화물차122125515902331161
135특수차12932251024
136이륜차12108726310895182
137사륜오토바이(ATV)12111561
138원동기장치자전거1216065312125
139자전거1222956413146
140개인형이동장치(PM)12971226321
141건설기계121817452126
142농기계1283070
143기타/불명1222502516762