Overview

Dataset statistics

Number of variables5
Number of observations96
Missing cells45
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory45.4 B

Variable types

Numeric4
Categorical1

Dataset

Description- 노면 상태별 제주도 내 교통사고 통계를 제공합니다. - 데이터 제공처: TAAS 교통사고분석시스템
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/891

Alerts

사고 건수 is highly overall correlated with 부상자 수 and 2 other fieldsHigh correlation
부상자 수 is highly overall correlated with 사고 건수 and 2 other fieldsHigh correlation
사망자 수 is highly overall correlated with 사고 건수 and 2 other fieldsHigh correlation
노면 상태 is highly overall correlated with 사고 건수 and 2 other fieldsHigh correlation
사고 건수 has 15 (15.6%) missing valuesMissing
부상자 수 has 15 (15.6%) missing valuesMissing
사망자 수 has 15 (15.6%) missing valuesMissing
사망자 수 has 35 (36.5%) zerosZeros

Reproduction

Analysis started2023-12-11 19:35:41.673062
Analysis finished2023-12-11 19:35:43.820460
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준 연도
Real number (ℝ)

Distinct16
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2005
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T04:35:43.888711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12008.75
median2012.5
Q32016.25
95-th percentile2020
Maximum2020
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.6339707
Coefficient of variation (CV)0.0023025941
Kurtosis-1.2096465
Mean2012.5
Median Absolute Deviation (MAD)4
Skewness0
Sum193200
Variance21.473684
MonotonicityIncreasing
2023-12-12T04:35:44.029294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2005 6
 
6.2%
2014 6
 
6.2%
2020 6
 
6.2%
2019 6
 
6.2%
2018 6
 
6.2%
2017 6
 
6.2%
2016 6
 
6.2%
2015 6
 
6.2%
2013 6
 
6.2%
2006 6
 
6.2%
Other values (6) 36
37.5%
ValueCountFrequency (%)
2005 6
6.2%
2006 6
6.2%
2007 6
6.2%
2008 6
6.2%
2009 6
6.2%
2010 6
6.2%
2011 6
6.2%
2012 6
6.2%
2013 6
6.2%
2014 6
6.2%
ValueCountFrequency (%)
2020 6
6.2%
2019 6
6.2%
2018 6
6.2%
2017 6
6.2%
2016 6
6.2%
2015 6
6.2%
2014 6
6.2%
2013 6
6.2%
2012 6
6.2%
2011 6
6.2%

노면 상태
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
건조
16 
기타
16 
서리/결빙
16 
적설
16 
젖음/습기
16 

Length

Max length5
Median length2
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건조
2nd row기타
3rd row서리/결빙
4th row적설
5th row젖음/습기

Common Values

ValueCountFrequency (%)
건조 16
16.7%
기타 16
16.7%
서리/결빙 16
16.7%
적설 16
16.7%
젖음/습기 16
16.7%
해빙 16
16.7%

Length

2023-12-12T04:35:44.221927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:35:44.400283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건조 16
16.7%
기타 16
16.7%
서리/결빙 16
16.7%
적설 16
16.7%
젖음/습기 16
16.7%
해빙 16
16.7%

사고 건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)82.7%
Missing15
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean768.62963
Minimum1
Maximum3916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T04:35:44.560536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median73
Q3482
95-th percentile3663
Maximum3916
Range3915
Interquartile range (IQR)464

Descriptive statistics

Standard deviation1290.2874
Coefficient of variation (CV)1.6786855
Kurtosis0.72045709
Mean768.62963
Median Absolute Deviation (MAD)72
Skewness1.5711591
Sum62259
Variance1664841.6
MonotonicityNot monotonic
2023-12-12T04:35:44.722858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 4
 
4.2%
27 3
 
3.1%
1 3
 
3.1%
2 3
 
3.1%
13 2
 
2.1%
25 2
 
2.1%
23 2
 
2.1%
18 2
 
2.1%
24 2
 
2.1%
198 1
 
1.0%
Other values (57) 57
59.4%
(Missing) 15
 
15.6%
ValueCountFrequency (%)
1 3
3.1%
2 3
3.1%
3 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
11 1
 
1.0%
13 2
2.1%
14 4
4.2%
15 1
 
1.0%
ValueCountFrequency (%)
3916 1
1.0%
3869 1
1.0%
3728 1
1.0%
3712 1
1.0%
3663 1
1.0%
3646 1
1.0%
3590 1
1.0%
3585 1
1.0%
3142 1
1.0%
3131 1
1.0%

부상자 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)88.9%
Missing15
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean1169.6667
Minimum1
Maximum6016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T04:35:44.871906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median157
Q3792
95-th percentile5564
Maximum6016
Range6015
Interquartile range (IQR)761

Descriptive statistics

Standard deviation1947.3811
Coefficient of variation (CV)1.6649027
Kurtosis0.77689256
Mean1169.6667
Median Absolute Deviation (MAD)149
Skewness1.5801184
Sum94743
Variance3792293.3
MonotonicityNot monotonic
2023-12-12T04:35:45.024978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 3
 
3.1%
2 3
 
3.1%
29 2
 
2.1%
66 2
 
2.1%
28 2
 
2.1%
4 2
 
2.1%
40 2
 
2.1%
64 1
 
1.0%
327 1
 
1.0%
5564 1
 
1.0%
Other values (62) 62
64.6%
(Missing) 15
 
15.6%
ValueCountFrequency (%)
1 1
 
1.0%
2 3
3.1%
4 2
2.1%
5 1
 
1.0%
8 1
 
1.0%
11 1
 
1.0%
13 1
 
1.0%
17 1
 
1.0%
18 1
 
1.0%
24 3
3.1%
ValueCountFrequency (%)
6016 1
1.0%
5827 1
1.0%
5726 1
1.0%
5684 1
1.0%
5564 1
1.0%
5506 1
1.0%
5492 1
1.0%
5413 1
1.0%
4846 1
1.0%
4564 1
1.0%

사망자 수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)34.6%
Missing15
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean17.802469
Minimum0
Maximum93
Zeros35
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T04:35:45.183338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q316
95-th percentile85
Maximum93
Range93
Interquartile range (IQR)16

Descriptive statistics

Standard deviation29.183651
Coefficient of variation (CV)1.6393036
Kurtosis0.8297607
Mean17.802469
Median Absolute Deviation (MAD)1
Skewness1.5569708
Sum1442
Variance851.68549
MonotonicityNot monotonic
2023-12-12T04:35:45.339139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 35
36.5%
1 8
 
8.3%
85 3
 
3.1%
18 3
 
3.1%
12 3
 
3.1%
87 2
 
2.1%
6 2
 
2.1%
5 2
 
2.1%
3 2
 
2.1%
13 2
 
2.1%
Other values (18) 19
19.8%
(Missing) 15
15.6%
ValueCountFrequency (%)
0 35
36.5%
1 8
 
8.3%
2 1
 
1.0%
3 2
 
2.1%
5 2
 
2.1%
6 2
 
2.1%
10 1
 
1.0%
11 1
 
1.0%
12 3
 
3.1%
13 2
 
2.1%
ValueCountFrequency (%)
93 1
 
1.0%
87 2
2.1%
85 3
3.1%
84 1
 
1.0%
77 1
 
1.0%
75 1
 
1.0%
69 1
 
1.0%
66 1
 
1.0%
63 1
 
1.0%
62 1
 
1.0%

Interactions

2023-12-12T04:35:43.078357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:41.866134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.264371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.687465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:43.179628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:41.971980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.364070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.797335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:43.292832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.079204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.467412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.899954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:43.396024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.176980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.581024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:42.996042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:35:45.448897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도노면 상태사고 건수부상자 수사망자 수
기준 연도1.0000.0000.0000.0000.149
노면 상태0.0001.0000.8950.9020.771
사고 건수0.0000.8951.0000.9820.906
부상자 수0.0000.9020.9821.0000.915
사망자 수0.1490.7710.9060.9151.000
2023-12-12T04:35:45.909087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도사고 건수부상자 수사망자 수노면 상태
기준 연도1.0000.0620.043-0.0240.000
사고 건수0.0621.0000.9900.9130.543
부상자 수0.0430.9901.0000.9100.557
사망자 수-0.0240.9130.9101.0000.562
노면 상태0.0000.5430.5570.5621.000

Missing values

2023-12-12T04:35:43.520177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:35:43.641839image/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.
2023-12-12T04:35:43.753376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준 연도노면 상태사고 건수부상자 수사망자 수
02005건조2742414993
12005기타11131
22005서리/결빙821861
32005적설25590
42005젖음/습기30650913
52005해빙<NA><NA><NA>
62006건조2796424885
72006기타340
82006서리/결빙20380
92006적설13400
기준 연도노면 상태사고 건수부상자 수사망자 수
862019서리/결빙220
872019적설120
882019젖음/습기45273210
892019해빙<NA><NA><NA>
902020건조3590550662
912020기타13280
922020서리/결빙19260
932020적설14180
942020젖음/습기3945536
952020해빙<NA><NA><NA>