Overview

Dataset statistics

Number of variables5
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory47.0 B

Variable types

Numeric4
Categorical1

Dataset

Description졸음운전, 교통사고, 졸음, 교통사고 현황, 교통사고 사건 사망 부상자 현황2017년부터 2022년까지의 도로종류, 사고건수, 사망자수, 부상자수 데이터
Author경찰청
URLhttps://www.data.go.kr/data/15047952/fileData.do

Alerts

사고_건 is highly overall correlated with 부상_명High correlation
부상_명 is highly overall correlated with 사고_건 and 1 other fieldsHigh correlation
도로종류 is highly overall correlated with 부상_명High correlation
사망_명 has 1 (2.4%) zerosZeros

Reproduction

Analysis started2024-03-14 23:37:42.668425
Analysis finished2024-03-14 23:37:46.685149
Duration4.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T08:37:46.782231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7285268
Coefficient of variation (CV)0.00085591819
Kurtosis-1.275956
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum84819
Variance2.9878049
MonotonicityIncreasing
2024-03-15T08:37:47.037282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 7
16.7%
2018 7
16.7%
2019 7
16.7%
2020 7
16.7%
2021 7
16.7%
2022 7
16.7%
ValueCountFrequency (%)
2017 7
16.7%
2018 7
16.7%
2019 7
16.7%
2020 7
16.7%
2021 7
16.7%
2022 7
16.7%
ValueCountFrequency (%)
2022 7
16.7%
2021 7
16.7%
2020 7
16.7%
2019 7
16.7%
2018 7
16.7%
2017 7
16.7%

도로종류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size464.0 B
일반국도
6 
지방도
6 
특별광역시도
6 
시도
6 
군도
6 
Other values (2)
12 

Length

Max length6
Median length4
Mean length3.2857143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반국도
2nd row지방도
3rd row특별광역시도
4th row시도
5th row군도

Common Values

ValueCountFrequency (%)
일반국도 6
14.3%
지방도 6
14.3%
특별광역시도 6
14.3%
시도 6
14.3%
군도 6
14.3%
고속국도 6
14.3%
기타 6
14.3%

Length

2024-03-15T08:37:47.501501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:37:47.833644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반국도 6
14.3%
지방도 6
14.3%
특별광역시도 6
14.3%
시도 6
14.3%
군도 6
14.3%
고속국도 6
14.3%
기타 6
14.3%

사고_건
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.71429
Minimum21
Maximum876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T08:37:48.123742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile61.45
Q1112.25
median217.5
Q3430.5
95-th percentile643.85
Maximum876
Range855
Interquartile range (IQR)318.25

Descriptive statistics

Standard deviation221.13557
Coefficient of variation (CV)0.76859434
Kurtosis0.18198445
Mean287.71429
Median Absolute Deviation (MAD)131
Skewness0.9587973
Sum12084
Variance48900.941
MonotonicityNot monotonic
2024-03-15T08:37:48.448090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
363 2
 
4.8%
147 2
 
4.8%
453 1
 
2.4%
644 1
 
2.4%
111 1
 
2.4%
138 1
 
2.4%
61 1
 
2.4%
342 1
 
2.4%
199 1
 
2.4%
522 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
21 1
2.4%
30 1
2.4%
61 1
2.4%
70 1
2.4%
71 1
2.4%
72 1
2.4%
88 1
2.4%
91 1
2.4%
101 1
2.4%
104 1
2.4%
ValueCountFrequency (%)
876 1
2.4%
825 1
2.4%
644 1
2.4%
641 1
2.4%
590 1
2.4%
577 1
2.4%
522 1
2.4%
514 1
2.4%
498 1
2.4%
470 1
2.4%

사망_명
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5
Minimum0
Maximum29
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T08:37:48.852715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q314
95-th percentile21.9
Maximum29
Range29
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.2524176
Coefficient of variation (CV)0.76341238
Kurtosis-0.30921958
Mean9.5
Median Absolute Deviation (MAD)6
Skewness0.58710644
Sum399
Variance52.597561
MonotonicityNot monotonic
2024-03-15T08:37:49.326332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 6
14.3%
8 3
 
7.1%
4 3
 
7.1%
2 3
 
7.1%
13 3
 
7.1%
14 3
 
7.1%
7 3
 
7.1%
12 2
 
4.8%
11 2
 
4.8%
3 2
 
4.8%
Other values (10) 12
28.6%
ValueCountFrequency (%)
0 1
 
2.4%
1 6
14.3%
2 3
7.1%
3 2
 
4.8%
4 3
7.1%
6 1
 
2.4%
7 3
7.1%
8 3
7.1%
10 1
 
2.4%
11 2
 
4.8%
ValueCountFrequency (%)
29 1
 
2.4%
22 2
4.8%
20 1
 
2.4%
19 1
 
2.4%
18 2
4.8%
17 1
 
2.4%
16 1
 
2.4%
14 3
7.1%
13 3
7.1%
12 2
4.8%

부상_명
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.85714
Minimum41
Maximum1629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T08:37:49.922120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile96
Q1192.5
median410.5
Q3786.5
95-th percentile1204.2
Maximum1629
Range1588
Interquartile range (IQR)594

Descriptive statistics

Standard deviation419.21312
Coefficient of variation (CV)0.76518692
Kurtosis0.11777436
Mean547.85714
Median Absolute Deviation (MAD)275
Skewness0.91395577
Sum23010
Variance175739.64
MonotonicityNot monotonic
2024-03-15T08:37:50.618353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
698 2
 
4.8%
169 2
 
4.8%
137 1
 
2.4%
1122 1
 
2.4%
188 1
 
2.4%
326 1
 
2.4%
95 1
 
2.4%
687 1
 
2.4%
353 1
 
2.4%
976 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
41 1
2.4%
76 1
2.4%
95 1
2.4%
115 1
2.4%
118 1
2.4%
127 1
2.4%
137 1
2.4%
169 2
4.8%
184 1
2.4%
188 1
2.4%
ValueCountFrequency (%)
1629 1
2.4%
1594 1
2.4%
1205 1
2.4%
1189 1
2.4%
1125 1
2.4%
1122 1
2.4%
976 1
2.4%
964 1
2.4%
960 1
2.4%
901 1
2.4%

Interactions

2024-03-15T08:37:45.610806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:43.000899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:44.126019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:45.011362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:45.771206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:43.254192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:44.370331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:45.161087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:45.978595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:43.498221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:44.579398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:45.296892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:46.221519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:43.803652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:44.722243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:37:45.446542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:37:50.915658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도도로종류사고_건사망_명부상_명
연도1.0000.0000.2440.3100.092
도로종류0.0001.0000.6960.4910.810
사고_건0.2440.6961.0000.1910.920
사망_명0.3100.4910.1911.0000.487
부상_명0.0920.8100.9200.4871.000
2024-03-15T08:37:51.239580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도사고_건사망_명부상_명도로종류
연도1.0000.030-0.1290.0020.000
사고_건0.0301.0000.4330.9920.457
사망_명-0.1290.4331.0000.4340.268
부상_명0.0020.9920.4341.0000.588
도로종류0.0000.4570.2680.5881.000

Missing values

2024-03-15T08:37:46.452091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:37:46.619552image/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

연도도로종류사고_건사망_명부상_명
02017일반국도36319698
12017지방도23010451
22017특별광역시도590121189
32017시도577111125
42017군도1013169
52017고속국도12022273
62017기타21041
72018일반국도24918487
82018지방도17011359
92018특별광역시도3294710
연도도로종류사고_건사망_명부상_명
322021군도8812137
332021고속국도1478295
342021기타721118
352022일반국도35014656
362022지방도2057377
372022특별광역시도5144960
382022시도4706901
392022군도914169
402022고속국도14913348
412022기타701127