Overview

Dataset statistics

Number of variables7
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory63.4 B

Variable types

Categorical1
Numeric6

Dataset

Description- 가해운전자 연령층별, 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/15070199/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 3 other fieldsHigh correlation
가해자연령층 is highly overall correlated with 사고건수 and 3 other fieldsHigh correlation
사고건수 has unique valuesUnique
사망자수 has 12 (12.5%) zerosZeros

Reproduction

Analysis started2023-12-12 04:03:50.795306
Analysis finished2023-12-12 04:03:55.838083
Duration5.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가해자연령층
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size900.0 B
20세이하
12 
21-30세
12 
31-40세
12 
41-50세
12 
51-60세
12 
Other values (3)
36 

Length

Max length6
Median length6
Mean length5.25
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20세이하
2nd row20세이하
3rd row20세이하
4th row20세이하
5th row20세이하

Common Values

ValueCountFrequency (%)
20세이하 12
12.5%
21-30세 12
12.5%
31-40세 12
12.5%
41-50세 12
12.5%
51-60세 12
12.5%
61-64세 12
12.5%
65세이상 12
12.5%
불명 12
12.5%

Length

2023-12-12T13:03:55.921768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:03:56.076281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20세이하 12
12.5%
21-30세 12
12.5%
31-40세 12
12.5%
41-50세 12
12.5%
51-60세 12
12.5%
61-64세 12
12.5%
65세이상 12
12.5%
불명 12
12.5%

발생월
Real number (ℝ)

Distinct12
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:03:56.258615image/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 deviation3.4701737
Coefficient of variation (CV)0.53387287
Kurtosis-1.2174168
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum624
Variance12.042105
MonotonicityNot monotonic
2023-12-12T13:03:56.389993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8
8.3%
2 8
8.3%
3 8
8.3%
4 8
8.3%
5 8
8.3%
6 8
8.3%
7 8
8.3%
8 8
8.3%
9 8
8.3%
10 8
8.3%
Other values (2) 16
16.7%
ValueCountFrequency (%)
1 8
8.3%
2 8
8.3%
3 8
8.3%
4 8
8.3%
5 8
8.3%
6 8
8.3%
7 8
8.3%
8 8
8.3%
9 8
8.3%
10 8
8.3%
ValueCountFrequency (%)
12 8
8.3%
11 8
8.3%
10 8
8.3%
9 8
8.3%
8 8
8.3%
7 8
8.3%
6 8
8.3%
5 8
8.3%
4 8
8.3%
3 8
8.3%

사고건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2050.375
Minimum172
Maximum4156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:03:56.590527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile222
Q11012.25
median2301
Q32892.5
95-th percentile3797
Maximum4156
Range3984
Interquartile range (IQR)1880.25

Descriptive statistics

Standard deviation1155.3294
Coefficient of variation (CV)0.56347227
Kurtosis-0.98972263
Mean2050.375
Median Absolute Deviation (MAD)665.5
Skewness-0.23452223
Sum196836
Variance1334786.1
MonotonicityNot monotonic
2023-12-12T13:03:56.793403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
435 1
 
1.0%
2849 1
 
1.0%
1687 1
 
1.0%
1703 1
 
1.0%
1855 1
 
1.0%
1751 1
 
1.0%
1681 1
 
1.0%
1705 1
 
1.0%
1691 1
 
1.0%
1735 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
172 1
1.0%
183 1
1.0%
211 1
1.0%
215 1
1.0%
216 1
1.0%
224 1
1.0%
231 1
1.0%
233 1
1.0%
243 1
1.0%
247 1
1.0%
ValueCountFrequency (%)
4156 1
1.0%
4001 1
1.0%
3989 1
1.0%
3911 1
1.0%
3872 1
1.0%
3772 1
1.0%
3747 1
1.0%
3733 1
1.0%
3683 1
1.0%
3677 1
1.0%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.489583
Minimum0
Maximum78
Zeros12
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:03:56.995470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median28.5
Q338
95-th percentile68.5
Maximum78
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.029449
Coefficient of variation (CV)0.70304465
Kurtosis-0.30241101
Mean28.489583
Median Absolute Deviation (MAD)12
Skewness0.42732356
Sum2735
Variance401.17884
MonotonicityNot monotonic
2023-12-12T13:03:57.176220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 12
 
12.5%
29 6
 
6.2%
37 5
 
5.2%
6 4
 
4.2%
30 4
 
4.2%
23 3
 
3.1%
8 3
 
3.1%
22 3
 
3.1%
26 3
 
3.1%
28 3
 
3.1%
Other values (36) 50
52.1%
ValueCountFrequency (%)
0 12
12.5%
2 1
 
1.0%
3 1
 
1.0%
5 1
 
1.0%
6 4
 
4.2%
7 2
 
2.1%
8 3
 
3.1%
16 2
 
2.1%
17 1
 
1.0%
19 2
 
2.1%
ValueCountFrequency (%)
78 1
1.0%
73 1
1.0%
72 1
1.0%
71 1
1.0%
70 1
1.0%
68 1
1.0%
64 1
1.0%
61 1
1.0%
59 2
2.1%
56 1
1.0%

중상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean538.69792
Minimum15
Maximum1152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:03:57.341225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile24.5
Q1305
median585.5
Q3751.5
95-th percentile1019
Maximum1152
Range1137
Interquartile range (IQR)446.5

Descriptive statistics

Standard deviation314.61693
Coefficient of variation (CV)0.5840322
Kurtosis-0.87556238
Mean538.69792
Median Absolute Deviation (MAD)188.5
Skewness-0.20093338
Sum51715
Variance98983.813
MonotonicityNot monotonic
2023-12-12T13:03:57.524741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 2
 
2.1%
29 2
 
2.1%
167 2
 
2.1%
429 2
 
2.1%
123 1
 
1.0%
918 1
 
1.0%
483 1
 
1.0%
440 1
 
1.0%
420 1
 
1.0%
472 1
 
1.0%
Other values (82) 82
85.4%
ValueCountFrequency (%)
15 1
1.0%
16 1
1.0%
18 1
1.0%
20 1
1.0%
23 1
1.0%
25 2
2.1%
28 1
1.0%
29 2
2.1%
33 1
1.0%
44 1
1.0%
ValueCountFrequency (%)
1152 1
1.0%
1125 1
1.0%
1074 1
1.0%
1044 1
1.0%
1043 1
1.0%
1011 1
1.0%
1004 1
1.0%
972 1
1.0%
971 1
1.0%
968 1
1.0%

경상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2212.8125
Minimum121
Maximum4552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:03:57.705220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile171.5
Q11030
median2540
Q33137
95-th percentile4132
Maximum4552
Range4431
Interquartile range (IQR)2107

Descriptive statistics

Standard deviation1282.1045
Coefficient of variation (CV)0.57940044
Kurtosis-0.99703
Mean2212.8125
Median Absolute Deviation (MAD)723
Skewness-0.29491987
Sum212430
Variance1643792
MonotonicityNot monotonic
2023-12-12T13:03:57.895424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1945 2
 
2.1%
458 1
 
1.0%
349 1
 
1.0%
1823 1
 
1.0%
2048 1
 
1.0%
1863 1
 
1.0%
1928 1
 
1.0%
1915 1
 
1.0%
1806 1
 
1.0%
1745 1
 
1.0%
Other values (85) 85
88.5%
ValueCountFrequency (%)
121 1
1.0%
144 1
1.0%
150 1
1.0%
167 1
1.0%
170 1
1.0%
172 1
1.0%
184 1
1.0%
187 1
1.0%
188 1
1.0%
196 1
1.0%
ValueCountFrequency (%)
4552 1
1.0%
4443 1
1.0%
4231 1
1.0%
4167 1
1.0%
4159 1
1.0%
4123 1
1.0%
4108 1
1.0%
4094 1
1.0%
3974 1
1.0%
3937 1
1.0%

부상신고자수
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.9375
Minimum44
Maximum389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:03:58.032075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile53.5
Q1108.75
median200
Q3247.25
95-th percentile320.25
Maximum389
Range345
Interquartile range (IQR)138.5

Descriptive statistics

Standard deviation87.953314
Coefficient of variation (CV)0.47816957
Kurtosis-0.93449725
Mean183.9375
Median Absolute Deviation (MAD)68
Skewness0.01940077
Sum17658
Variance7735.7855
MonotonicityNot monotonic
2023-12-12T13:03:58.226212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 2
 
2.1%
224 2
 
2.1%
214 2
 
2.1%
97 2
 
2.1%
117 2
 
2.1%
260 2
 
2.1%
56 2
 
2.1%
200 2
 
2.1%
220 2
 
2.1%
149 1
 
1.0%
Other values (77) 77
80.2%
ValueCountFrequency (%)
44 1
1.0%
46 1
1.0%
50 1
1.0%
52 2
2.1%
54 1
1.0%
56 2
2.1%
58 1
1.0%
60 1
1.0%
62 1
1.0%
63 1
1.0%
ValueCountFrequency (%)
389 1
1.0%
379 1
1.0%
347 1
1.0%
323 1
1.0%
321 1
1.0%
320 1
1.0%
303 1
1.0%
294 1
1.0%
291 1
1.0%
287 1
1.0%

Interactions

2023-12-12T13:03:54.856002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.053262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.762985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.560834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.250271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.882217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:54.981161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.156400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.876532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.656963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.354966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:54.298499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:55.105606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.278763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.049740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.767952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.455819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:54.420317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:55.228253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.434887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.165429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.886040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.569387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:54.544015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:55.338231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.552899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.294733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.011528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.687493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:54.650910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:55.462212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:51.646911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:52.422903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.128571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:53.792586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:54.735561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:03:58.333344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가해자연령층발생월사고건수사망자수중상자수경상자수부상신고자수
가해자연령층1.0000.0000.8690.8120.8870.8420.742
발생월0.0001.0000.0000.0000.0000.0000.204
사고건수0.8690.0001.0000.8920.9720.9890.922
사망자수0.8120.0000.8921.0000.9120.8840.807
중상자수0.8870.0000.9720.9121.0000.9600.902
경상자수0.8420.0000.9890.8840.9601.0000.920
부상신고자수0.7420.2040.9220.8070.9020.9201.000
2023-12-12T13:03:58.447683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월사고건수사망자수중상자수경상자수부상신고자수가해자연령층
발생월1.0000.1430.1590.0930.1700.2480.000
사고건수0.1431.0000.9050.9860.9940.9540.656
사망자수0.1590.9051.0000.9130.8980.8790.560
중상자수0.0930.9860.9131.0000.9750.9390.690
경상자수0.1700.9940.8980.9751.0000.9550.607
부상신고자수0.2480.9540.8790.9390.9551.0000.469
가해자연령층0.0000.6560.5600.6900.6070.4691.000

Missing values

2023-12-12T13:03:55.625184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:03:55.782884image/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

가해자연령층발생월사고건수사망자수중상자수경상자수부상신고자수
020세이하1435612345852
120세이하2357210234954
220세이하3473314146473
320세이하4580715455997
420세이하57136191631133
520세이하66685170590117
620세이하76396194541112
720세이하8557715049986
820세이하96098167527114
920세이하10618816754989
가해자연령층발생월사고건수사망자수중상자수경상자수부상신고자수
86불명318301614444
87불명421101517046
88불명525902919962
89불명621602516750
90불명721502015063
91불명823303318758
92불명928004419665
93불명1022402917260
94불명1124302820569
95불명1224702518466