Overview

Dataset statistics

Number of variables8
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory73.4 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description* 부문별 대형 교통사고(2018)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094164/fileData.do

Alerts

사망자수 is highly overall correlated with 부상자수 and 1 other fieldsHigh correlation
부상자수 is highly overall correlated with 사망자수 and 3 other fieldsHigh correlation
경상 is highly overall correlated with 사망자수 and 2 other fieldsHigh correlation
부상신고 is highly overall correlated with 부상자수 and 1 other fieldsHigh correlation
발생건수 is highly overall correlated with 부상자수 and 2 other fieldsHigh correlation
발생건수 is highly imbalanced (62.6%)Imbalance
사망자수 has 21 (53.8%) zerosZeros
부상자수 has 2 (5.1%) zerosZeros
중상 has 9 (23.1%) zerosZeros
경상 has 7 (17.9%) zerosZeros
부상신고 has 26 (66.7%) zerosZeros

Reproduction

Analysis started2023-12-11 23:18:57.732916
Analysis finished2023-12-11 23:19:00.756575
Duration3.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
경기
8 
서울
6 
충남
6 
전남
4 
충북
3 
Other values (9)
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique6 ?
Unique (%)15.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기 8
20.5%
서울 6
15.4%
충남 6
15.4%
전남 4
10.3%
충북 3
 
7.7%
강원 2
 
5.1%
경남 2
 
5.1%
대전 2
 
5.1%
부산 1
 
2.6%
전북 1
 
2.6%
Other values (4) 4
10.3%

Length

2023-12-12T08:19:00.834469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 8
20.5%
서울 6
15.4%
충남 6
15.4%
전남 4
10.3%
충북 3
 
7.7%
강원 2
 
5.1%
경남 2
 
5.1%
대전 2
 
5.1%
부산 1
 
2.6%
전북 1
 
2.6%
Other values (4) 4
10.3%

시군구
Text

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T08:19:01.019497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9487179
Min length2

Characters and Unicode

Total characters115
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)94.9%

Sample

1st row강서구
2nd row강남구
3rd row서초구
4th row양천구
5th row강북구
ValueCountFrequency (%)
북구 2
 
5.1%
전주시 1
 
2.6%
동구 1
 
2.6%
순천시 1
 
2.6%
아산시 1
 
2.6%
공주시 1
 
2.6%
서산시 1
 
2.6%
홍성군 1
 
2.6%
예산군 1
 
2.6%
여수시 1
 
2.6%
Other values (28) 28
71.8%
2023-12-12T08:19:01.331890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 18
15.7%
구 12
 
10.4%
군 9
 
7.8%
천 8
 
7.0%
성 6
 
5.2%
주 6
 
5.2%
산 4
 
3.5%
양 4
 
3.5%
북 3
 
2.6%
진 3
 
2.6%
Other values (31) 42
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 18
15.7%
구 12
 
10.4%
군 9
 
7.8%
천 8
 
7.0%
성 6
 
5.2%
주 6
 
5.2%
산 4
 
3.5%
양 4
 
3.5%
북 3
 
2.6%
진 3
 
2.6%
Other values (31) 42
36.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 18
15.7%
구 12
 
10.4%
군 9
 
7.8%
천 8
 
7.0%
성 6
 
5.2%
주 6
 
5.2%
산 4
 
3.5%
양 4
 
3.5%
북 3
 
2.6%
진 3
 
2.6%
Other values (31) 42
36.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 18
15.7%
구 12
 
10.4%
군 9
 
7.8%
천 8
 
7.0%
성 6
 
5.2%
주 6
 
5.2%
산 4
 
3.5%
양 4
 
3.5%
북 3
 
2.6%
진 3
 
2.6%
Other values (31) 42
36.5%

발생건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
1
34 
2
 
2
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 34
87.2%
2 2
 
5.1%
3 2
 
5.1%
4 1
 
2.6%

Length

2023-12-12T08:19:01.435808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:19:01.513916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 34
87.2%
2 2
 
5.1%
3 2
 
5.1%
4 1
 
2.6%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3846154
Minimum0
Maximum8
Zeros21
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T08:19:01.591451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8298028
Coefficient of variation (CV)1.3215242
Kurtosis2.6892328
Mean1.3846154
Median Absolute Deviation (MAD)0
Skewness1.4306092
Sum54
Variance3.3481781
MonotonicityNot monotonic
2023-12-12T08:19:01.691860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 21
53.8%
3 11
28.2%
1 3
 
7.7%
4 2
 
5.1%
8 1
 
2.6%
2 1
 
2.6%
ValueCountFrequency (%)
0 21
53.8%
1 3
 
7.7%
2 1
 
2.6%
3 11
28.2%
4 2
 
5.1%
8 1
 
2.6%
ValueCountFrequency (%)
8 1
 
2.6%
4 2
 
5.1%
3 11
28.2%
2 1
 
2.6%
1 3
 
7.7%
0 21
53.8%

부상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.666667
Minimum0
Maximum98
Zeros2
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T08:19:01.783682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q15
median22
Q331.5
95-th percentile59.4
Maximum98
Range98
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation21.665317
Coefficient of variation (CV)0.87832367
Kurtosis2.9025913
Mean24.666667
Median Absolute Deviation (MAD)15
Skewness1.381658
Sum962
Variance469.38596
MonotonicityNot monotonic
2023-12-12T08:19:01.884804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 5
 
12.8%
21 4
 
10.3%
31 3
 
7.7%
19 2
 
5.1%
27 2
 
5.1%
26 2
 
5.1%
0 2
 
5.1%
5 2
 
5.1%
2 2
 
5.1%
49 1
 
2.6%
Other values (14) 14
35.9%
ValueCountFrequency (%)
0 2
 
5.1%
1 5
12.8%
2 2
 
5.1%
5 2
 
5.1%
11 1
 
2.6%
19 2
 
5.1%
20 1
 
2.6%
21 4
10.3%
22 1
 
2.6%
24 1
 
2.6%
ValueCountFrequency (%)
98 1
2.6%
81 1
2.6%
57 1
2.6%
49 1
2.6%
45 1
2.6%
42 1
2.6%
40 1
2.6%
39 1
2.6%
37 1
2.6%
32 1
2.6%

중상
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4871795
Minimum0
Maximum19
Zeros9
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T08:19:01.972624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile14.2
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.154823
Coefficient of variation (CV)1.1487891
Kurtosis0.76944953
Mean4.4871795
Median Absolute Deviation (MAD)2
Skewness1.3042831
Sum175
Variance26.5722
MonotonicityNot monotonic
2023-12-12T08:19:02.070260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 9
23.1%
1 6
15.4%
2 5
12.8%
4 4
10.3%
5 3
 
7.7%
3 2
 
5.1%
14 2
 
5.1%
10 2
 
5.1%
11 1
 
2.6%
19 1
 
2.6%
Other values (4) 4
10.3%
ValueCountFrequency (%)
0 9
23.1%
1 6
15.4%
2 5
12.8%
3 2
 
5.1%
4 4
10.3%
5 3
 
7.7%
7 1
 
2.6%
8 1
 
2.6%
10 2
 
5.1%
11 1
 
2.6%
ValueCountFrequency (%)
19 1
 
2.6%
16 1
 
2.6%
14 2
5.1%
13 1
 
2.6%
11 1
 
2.6%
10 2
5.1%
8 1
 
2.6%
7 1
 
2.6%
5 3
7.7%
4 4
10.3%

경상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.307692
Minimum0
Maximum78
Zeros7
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T08:19:02.181892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median14
Q324.5
95-th percentile42.2
Maximum78
Range78
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.463922
Coefficient of variation (CV)1.0095801
Kurtosis3.7405294
Mean16.307692
Median Absolute Deviation (MAD)12
Skewness1.5203036
Sum636
Variance271.06073
MonotonicityNot monotonic
2023-12-12T08:19:02.318910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 7
17.9%
18 4
 
10.3%
1 3
 
7.7%
2 2
 
5.1%
24 2
 
5.1%
29 2
 
5.1%
10 2
 
5.1%
35 1
 
2.6%
6 1
 
2.6%
42 1
 
2.6%
Other values (14) 14
35.9%
ValueCountFrequency (%)
0 7
17.9%
1 3
7.7%
2 2
 
5.1%
4 1
 
2.6%
6 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
10 2
 
5.1%
11 1
 
2.6%
14 1
 
2.6%
ValueCountFrequency (%)
78 1
2.6%
44 1
2.6%
42 1
2.6%
36 1
2.6%
35 1
2.6%
29 2
5.1%
28 1
2.6%
27 1
2.6%
25 1
2.6%
24 2
5.1%

부상신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8717949
Minimum0
Maximum38
Zeros26
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T08:19:02.431223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile22.5
Maximum38
Range38
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation8.6242604
Coefficient of variation (CV)2.227458
Kurtosis6.8997878
Mean3.8717949
Median Absolute Deviation (MAD)0
Skewness2.6545946
Sum151
Variance74.377868
MonotonicityNot monotonic
2023-12-12T08:19:02.550177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 26
66.7%
3 2
 
5.1%
1 2
 
5.1%
11 1
 
2.6%
19 1
 
2.6%
6 1
 
2.6%
2 1
 
2.6%
4 1
 
2.6%
22 1
 
2.6%
27 1
 
2.6%
Other values (2) 2
 
5.1%
ValueCountFrequency (%)
0 26
66.7%
1 2
 
5.1%
2 1
 
2.6%
3 2
 
5.1%
4 1
 
2.6%
6 1
 
2.6%
11 1
 
2.6%
14 1
 
2.6%
19 1
 
2.6%
22 1
 
2.6%
ValueCountFrequency (%)
38 1
2.6%
27 1
2.6%
22 1
2.6%
19 1
2.6%
14 1
2.6%
11 1
2.6%
6 1
2.6%
4 1
2.6%
3 2
5.1%
2 1
2.6%

Interactions

2023-12-12T08:19:00.172227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.251065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.745394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.206120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.699689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:00.278893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.338311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.844999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.294480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.791812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:00.369583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.448394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.916221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.415231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.876965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:00.454255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.540208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.002278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.513484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.970667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:00.529087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:58.635130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.086897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:59.614859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:00.065269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:19:02.646832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도시군구발생건수사망자수부상자수중상경상부상신고
시도1.0000.0000.0000.7340.0000.3320.1120.000
시군구0.0001.0001.0000.0000.8600.0000.0001.000
발생건수0.0001.0001.0000.0000.9970.7210.7740.685
사망자수0.7340.0000.0001.0000.7620.8380.4050.000
부상자수0.0000.8600.9970.7621.0000.7410.8330.623
중상0.3320.0000.7210.8380.7411.0000.6360.000
경상0.1120.0000.7740.4050.8330.6361.0000.597
부상신고0.0001.0000.6850.0000.6230.0000.5971.000
2023-12-12T08:19:02.755839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도발생건수
시도1.0000.000
발생건수0.0001.000
2023-12-12T08:19:02.829471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사망자수부상자수중상경상부상신고시도발생건수
사망자수1.000-0.583-0.184-0.588-0.3290.4040.000
부상자수-0.5831.0000.3910.9210.5750.0000.777
중상-0.1840.3911.0000.372-0.0020.0760.473
경상-0.5880.9210.3721.0000.3420.0000.630
부상신고-0.3290.575-0.0020.3421.0000.0000.583
시도0.4040.0000.0760.0000.0001.0000.000
발생건수0.0000.7770.4730.6300.5830.0001.000

Missing values

2023-12-12T08:19:00.623467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:19:00.715938image/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

시도시군구발생건수사망자수부상자수중상경상부상신고
0서울강서구204911353
1서울강남구10211911
2서울서초구10210219
3서울양천구10313226
4서울강북구10324280
5서울금천구104014242
6부산동래구11195140
7경기부천시10275184
8경기평택시334502322
9경기화성시3181104427
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